title string | paper_decision string | review_1 string | rebuttals_1 string | review_2 string | rebuttals_2 string | review_3 string | rebuttals_3 string | review_4 string | rebuttals_4 string | global_rebuttals string | dataset_source string | conference_year int64 | review_5 string | rebuttals_5 string | review_6 string | rebuttals_6 string | review_7 string | rebuttals_7 string | review_8 string | rebuttals_8 string |
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Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense | Accept (poster) | Summary: This paper proposed a denoising pre-training framework NIM similar to MIM to improve adversarial defense ability. In particular, the input image is a noisy version of the original. The pre-training goal is to do denoising.
Strengths: This paper is easy to follow.
Weaknesses: * a) Novelty is limited. The ide... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We'd like to address your concerns:
> Novelty is limited. The idea proposed in this work is very similar to CIM [1].
There are many major differences between our work and [1], including
1) Purpose: Our goal is to explore how the generative pretraining paradi... | Summary: The authors introduce noise image modeling (NIM) as a self-supervised pretext task and demonstrate that their encoder-decoder architecture decreases the success of adversarial attacks by adding noise to a perturbed image and then denoising it. Their method is called $De^3$. Furthermore, the author conduct a ma... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. Here are our responses:
> The "flooding out effect" for very large sigma (140) seems not reasonable, since it corrupts the whole image.
> The reconstruction seems "too good to be true". This seems odd despite other efforts [1].
Given that noi... | Summary: The paper presents a straightforward approach for defending against adversarial attacks while incorporating pre-trained feature learning through the utilization of noisy images. Inspired by Masked Image Modeling (MIM), the paper replaces the masking pretext task by introducing a substantial amount of noise int... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and suggestions! Our answers are as follows:
> It would be beneficial to have a more extensive comparison by including well-known self-supervised features like SimCLR, MOCO, DINO, and others.
While we concur that the comparison would be a great direction to... | Summary: This paper proposes a novel adversarial defense method $De^3$ to utilize the strong denoising ability of NIM models. The proposed method first adds some Gaussian noise to the adversarial samples and then tries to reconstruct the original images. Experiments show the advantage of NIM over MIM in terms of advers... | Rebuttal 1:
Rebuttal: Thank you very much for your positive affirmation of our work and your constructive suggestions. Here are our responses.
> Although De3 can enhance adversarial defense, it increases computational cost during inference time. Note that there exists two encoders and one decoder in De3, can we distil... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a method called Noisy Image Modeling (NIM) as self-supervised learning to improve the adversarial robustness of pretrained features. NIM uses denoising as a pretext task and is effective in reconstructing noisy images for representation learning. In addition, the authors propose a defense t... | Rebuttal 1:
Rebuttal: We appreciate your approval and recognition of this paper. Our answers are listed below.
> The paper does not thoroughly analyze the computational cost of the proposed method. Adversarial training methods, such as the one proposed by Wang et al. [34], are known to be computationally expensive. It... | null | null | null | null | null | null |
Post-processing Private Synthetic Data for Improving Utility on Selected Measures | Accept (poster) | Summary: The paper considers how to obtain improved utility from synthetic data under differential privacy. Prior work has tried to achieve utility by incorporating utility directly into the generation of the synthetic data e.g., by making use of workload data. Here, the proposal is to perform post-hoc reweighting of... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful read of our paper and constructive comments!
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**Q1. Evaluation is on four benchmark data sets that may be considered "easy". It would be interesting to test this out on additional data, such as that used in recent synthetic data challenges by NIST.... | Summary: The paper proposes a DP post-processing method for synthetic data that weights the synthetic data to match user-selected utility measures on the real dataset. The utility measures on real data are measured with noise to make this post-processing DP. To find the synthetic data weights, the paper derives a close... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and for appreciating the value of the work!
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**Q1. Some important experimental details are missing.**
A1. We appreciate your constructive feedback regarding our experimental results! Please find the detailed responses to your questions below,... | Summary: The paper introduces a post-processing technique that enhances the utility of synthetic DP data with respect to selected measures.
The proposed technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using a stochastic first-order algorithm t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the kind comments and the encouragement!
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**Q1. More detailed discussion on the trade-offs involved in using the proposed post-processing technique and the impact of the technique on the computational complexity of the data synthesis process.**
A1. That’s a gre... | Summary: The paper under review introduces a technique aimed at enhancing the quality of differentially private synthetic data. Specifically, this approach is applicable when a private synthetic data set, generated by any available privacy-preserving mechanism, does not align with the original data set on certain key m... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review and for appreciating the merits of the work!
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**Q1. Comparison with PEP in [LVW21].**
A1. Thank you for highlighting this comparison. In contrast to PEP in [LVW21], our proposed solution offers several enhancements.
First, it is important... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for taking the time and effort to review our paper! We are delighted to learn that our paper was positively received, and the reviewers found that the idea of post-processing synthetic data presented in this paper is a compelling approach that boasts potent... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Symbolic Discovery of Optimization Algorithms | Accept (poster) | Summary: The paper introduces Lion, an automatic search strategy for deep learning optimizers. The paper claims that the resulting optimizer is more compute efficient than the baselines and achieves better accuracy on a variety of tasks from vision and language and model architectures. The search uses an evolutionary a... | Rebuttal 1:
Rebuttal: Thank you for your constructive and insightful comments!
> Program 4 is an automatic simplification of Program 8. However, the authors further do manual simplifications to arrive at the final optimizer in program 1. I understand that the sign simplification (from the code in red) might be hard to... | Summary: This paper proposes to formulate algorithm discovery as program search and discover optimization algorithms for deep neural network training. To bridge the large generalization gap between proxy and target tasks, the authors introduce program selection and simplification strategies. The proposed method discove... | Rebuttal 1:
Rebuttal: Thank you for your constructive and insightful comments!
> The computation resources required by the proposed search method is unaffordable for most research groups
We acknowledge that the search cost of Lion (3K TPU V2 days) is still significant, and improving its efficiency is an important fut... | Summary: In this paper, a new optimizer is designed by program search. The search process overcomes the problem of distribution differences for small data set proxies and large tasks. The new optimizer converges faster and at the same time has strong generalization over a wide range of models for different tasks.
Stre... | Rebuttal 1:
Rebuttal: Thank you for your constructive and insightful comments!
> The original algorithm obtained from the search is still more complex. It needs expert knowledge and labor cost for simplification. See the simplification of Program 4 to Program 1 for details.
We would like to point out that a large par... | Summary:
Summary: The authors' work undoubtedly stands as an exceptional and noteworthy contribution to the field, showcasing a plethora of remarkable achievements that have the potential to significantly influence and shape the landscape of optimization and extend their impact well beyond its boundaries. As I delve i... | Rebuttal 1:
Rebuttal: Thank you for your constructive and insightful comments!
> A more elegant solution would involve automating this process as an inherent aspect of defining the search space, further enhancing the overall methodology and augmenting its potential impact.
We would like to point out that a large par... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Curves for Deep Structured Gaussian Feature Models | Accept (poster) | Summary: This work focuses on the generalization performance of models utilizing multi-layered Gaussian random features. The study evaluates the impact of feature anisotropy, which is often overlooked due to common assumptions that features are generated using independent, identically distributed Gaussian weights. The ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful assessment of our work, and are gratified by their favorable comments.
### Weaknesses:
1. *The discussion to previous works are limited. There are many RFM works recently. Although they may focus on a different perspective, such as inductive biases or beh... | Summary: The authors investigate the exact asymptotics characterization of deep Gaussian feature models. They analyze depth-L linear Random Feature Models where the feature matrix is built as a product of L factors, each drawn from a matrix Gaussian distribution. Thanks to the replica method, they compute the generaliz... | Rebuttal 1:
Rebuttal: We sincerely appreciate the referee’s feedback on the clarity of our submitted manuscript, and will revise our manuscript in accordance with their valuable suggestions.
### Questions:
1. *The abstract is very concise. I would expand this section to help guide the reader, e.g., mention that you f... | Summary: The authors introduce correlations to weights of linear RFMs and study the generalization error.
This is done under the assumption data follows Gaussian distribution and linear model.
The authors provide a general expression for the limiting generalization error that recovers results from previous works as s... | Rebuttal 1:
Rebuttal: Thank you for your careful assessment of our work; we hope that our revisions to the Introduction further enhance its clarity.
### Weaknesses:
*The proofs could be included in the supplementary material which would allow for more text interpreting the results and providing the intuition for thei... | Summary: This work studies the asymptotic risk of deep linear random features models (RFMs), with a high dimensional analysis based on the replica method. It extends previous work on this topic by relaxing the standard i.i.d assumption for the Gaussian weights in each layer.
Several consequences of their anaysis are ... | Rebuttal 1:
Rebuttal: We thank the referee for their careful and favorable assessment of our work. We hope that they find that our revisions will strengthen the paper.
### Weaknesses:
*My main reservation regarding this paper is related to its scope [...]*
Thank you for this comment. As we acknowledge, our work is ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their careful assessment of our work. A commonly-expressed concern was that the Abstract and Introduction were too terse to provide adequate guidance and context to the reader, and that the paper would be clearer if we provided more discussion of each result. For our rev... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper derives the learning curves (generalization error vs number of samples) for random linear deep networks with Gaussian weights with non-trivial correlations. The correlations at the first layer control the performance of those networks, while all other layers (possibly of different widths) are equival... | Rebuttal 1:
Rebuttal: ### Strengths:
*Quality: the main result that in the noiseless case, correlations at the first layer improve performance, while correlations in the other layers degrade it (Lemmas 4.1 and 4.2) is interesting (and is not fully captured by the summary “representation learning at only the first layer... | null | null | null | null | null | null |
Generating Behaviorally Diverse Policies with Latent Diffusion Models | Accept (poster) | Summary: The paper presents a diffusion-based approach to compressing a policy archive discovered by a Quality-Diversity RL algorithm. The diffusion model operates in the latent space of a VAE and achieves high levels of compression together with a reasonable level of reconstruction. The algorithm is evaluated on a col... | Rebuttal 1:
Rebuttal: **The approach studied in the paper is limited to compression of the original archive...**
We agree that generalization to novel language instructions is an interesting direction for future work. However, generalizing to novel measure dimensions is not possible with the environments available. Th... | Summary: Quality-Diversity Reinforcement Learning generates a set of policies (here called the archive) that are learned to produce varied behaviors in the environment. These archives can be large, and this paper aims to compress a previously learned archive into a single model by leveraging a conditional diffusion mo... | Rebuttal 1:
Rebuttal: **Evaluation of the model is thorough,...**
There are two archive distillation methods most similar to ours, DCG-Map-Elites [1] and the QD-Transformer [2]. However, both of those methods are concurrent with ours and the code was not available for either of them at the time of submission. There ar... | Summary: This paper tried to solve the high space complexity in Quality Diversity and proposes a method that uses diffusion model to distill the archive into a single generative model based on policy parameters.
Strengths: * This paper leverages the generation power of diffusion model and condenses one model instead o... | Rebuttal 1:
Rebuttal: **Quality Diversity is not well-known. It's better to include a background section instead of including basic knowledge in the related work.**
We have included a brief background section in our related work section. Please see the global response for a detailed explanation of measures we used in Q... | Summary: This work presents a novel framework using latent diffusion models to distill the archive of polices into a single generative model over policy parameters. The latent diffusion model with VAE backbone compresses the high dimensional neural network (NN) parameters into a compact, making it possible to reconstru... | Rebuttal 1:
Rebuttal: **The performance and accuracy of the proposed method show significant discrepancies when applied under text conditions. Experiments show that the success rate of the method is influenced by the selection of text labels. This suggests that the model possesses limited understanding and generation c... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their in-depth feedback. Attached here is a pdf document with additional tables and figures.
**Measures in Quality Diversity:**
Thank you for the reviewers' feedback. We see that our explanation of measures is lacking. Below is a brief explanation of what measures a... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Uncertainty Quantification via Neural Posterior Principal Components | Accept (poster) | Summary: This work proposes to model the full covariance matrix of aleatoric uncertainty for per-pixel image restoration tasks using a low-rank approximation. The low-rank approximation is estimated by a separate network trained end-to-end with the original predictor. The PCs, which are usually computed sequentially, a... | Rebuttal 1:
Rebuttal: **Method proposition**
Our work proposes to output the top $K$ covariance eigenvectors/PCs directly **without** assuming a low-rank (LR) structure. Although we can use the resulting PCs $\\bf{W}$ to construct an LR covariance approx., at no point do we use this assumption. Our work is inspired by... | Summary: For uncertainty quantification in image recovery problems, the authors propose a way to train a neural network to produce estimates of the principle components of the posterior covariance matrix. Their approach starts with a neural network that produces the posterior mean and trains a new network (or a new pre... | Rebuttal 1:
Rebuttal: **Advantages of the proposed method compared to fast posterior samplers such as conditional GANs/Normalizing Flows.**
Thanks, this is an important point. Please note that even in the case of a fast conditional generative model capable of sampling from the posterior using a single neural function ... | Summary: This paper proposes using a deep neural network to predict the principle components (PCs), and the associated uncertainties, of the output directly, instead of just the most likely output. This is done by proposing a PC loss that ensures PCs are unit vectors and are orthogonal. The experimental results indicat... | Rebuttal 1:
Rebuttal: **The proposed method was only benchmarked on CelebA-HQ and not on MNIST/biological dataset. Could there be further comparisons on the biological dataset?**
The reason we did not compare to other techniques on the MNIST/biological datasets is that to the best of our knowledge, there exist no pre-... | Summary: This work proposes to do image recovery inference using posterior principal components. The posterior principal components are often regarded as a function of observed image, but estimating it from a single image is impossible. Therefore, this work proposes to learn the principal components directly by trainin... | Rebuttal 1:
Rebuttal: **There is no quality control on the predicted principal components**
Thanks for this important comment. Please note that as correctly mentioned in your summary, there exists no ground truth uncertainty in image restoration datasets. This is because each element in the dataset is comprised of a *... | Rebuttal 1:
Rebuttal: The PDF includes 2 figures containing the results of experiments proposed by the reviewers.
Pdf: /pdf/7e1f7ab4bc0ee5bb8f2c9b7bef83a2fe63205fde.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Regret Matching+: (In)Stability and Fast Convergence in Games | Accept (spotlight) | Summary: The paper pushed RM+ based algorithm better than $O(\sqrt T)$ theoretically the first time. Specifically, the authors proposed smooth RM+, conceptual RM+ and extragradient RM+ with theoretical regret guarantee.
Strengths: To the best of my knowledge, this paper is the first one to establish RM+ based algorith... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. Please see below for our response.
- _The proof intuition in the main text:_ We will make sure to provide more intuition of the proofs in the final version when an additional page is available.
- _Does CFR equipped with the new RM+ have constant ... | Summary: The authors study $\text{RM}^+$. They show that there are instances of loss sequences in variants of $\text{RM}^+$ unstable. The authors point out that the decisions of $\text{RM}^+$-based algorithms are performed by normalizing an aggregate payoff vector. Hence, if one inputs an instance that is "close" to th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and valuable comments. We will revise the citations, notations, and definitions based on your suggestions. Below we answer your questions.
- _How important is it to the overall community?_ Understanding how CFR+ works well in game solving is ... | Summary: The paper studies variants of the RM+ algorithm for learning in games. They show that RM+ does not satisfy stability, which is an important property for proving good regret bounds, if all players play according to the same algorithm. The paper "fixes" the instability of RM+ by considering a restarting and chop... | Rebuttal 1:
Rebuttal: We thank you for your time reviewing the paper. We now answer your question and remarks on the manuscript.
- Clarity: in the final version we will use the additional page to provide more details on the results in Section 5 (Conceptual RM+/ExRM+). This will improve the overall clarity of the paper... | Summary: This paper studies the RM+ algorithm and its variants in the context of learning in games.
- They first show that RM+ and predictive RM+ can be unstable in certain environments. Although the unstable player benefits from instability, the regret of other players may blow up because they are forced to make predi... | Rebuttal 1:
Rebuttal: We thank you for your time reviewing the paper.
- We have run additional numerical experiments to compare Optimistic OMD (OOMD) and Optimistic FTRL (OFTRL) with ExRM+, Stable and Smooth PRM+. We ran these algorithms for the $3 \times 3$ matrix game instance and for $10$ random matrix game instanc... | Rebuttal 1:
Rebuttal: We thank all the reviewers. We responded to each of your questions in individual comments. Here we upload a PDF of two figures to support those responses. Specifically, in the PDF, we plotted:
- Figure 1: Comparison of Optimistic OMD, Optimistic FTRL, ExRM+, Stable and Smooth PRM+ on random matri... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper focuses on investigating the stability of the Regret Matching+ based algorithm. It demonstrates that both RM+ and predictive RM+ algorithms exhibit instability, leading to significant regret for other players within a game setting. To address this issue, the authors propose two methods: restarting an... | Rebuttal 1:
Rebuttal: We thank you for your time reviewing the paper. In the final version of the paper, we will add a table to summarize the different regret guarantees for the algorithms introduced in this paper. Regarding your question: you are right that the stepsizes we use in the experiments are generally larger ... | null | null | null | null | null | null |
Mitigating Source Bias for Fairer Weak Supervision | Accept (poster) | Summary: This work found that unfair LFs in programmatic weak supervision could introduce bias to the resultant training labels, and proposed to address the bias via source bias mitigation and provided theoretical guarantee. Experimental results show that the effectiveness of the approaches in both synthetic and real d... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the thoughtful comments and reference. We will add the suggested paper in our updated draft.
* **On the choice of label models**: A remarkable property of our label model is that **it is only used for one step in our overall pipeline: mitigating source bias**. Once t... | Summary: *The paper studies the problem of unfairness introduced by weak supervision methods through noisy data augmentation. It proposes a mitigation strategy using counterfactuals that would create a more balanced/unbiased dataset.
* Paper shows that Labeling functions can be arbitrarily biased by preferring example... | Rebuttal 1:
Rebuttal: We thank the reviewer for the suggestions and noting the strengths of our work. We have added the suggested reference.
* **On modeling assumptions**. Indeed, our label model _may appear_, at first glance, to require strong assumptions. However, in fact it **has weaker assumptions compared to previ... | Summary: This paper proposes a novel bias mitigation technique to address the fairness issues in weak supervision settings. The core idea is to use a counterfactual fairness-based correction method. The authors theoretically show that the proposed method can improve both accuracy and fairness and this is also supporte... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for noting the strengths of our work and providing useful questions and comments.
* **On the potential drawbacks of the proposed method in the programmatic WS settings**: A possible drawback of the proposed method is that it requires high quality estimation of Monge... | Summary: The paper focuses on the bias issue in weak supervision. The paper shows the labeling functions of weak supervision may produce biased pseudo-labels, which is first empirically shown in the paper. Also, they theoretically show that even though a dataset has fair ground-truth labels, the weak supervision proces... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their kind words, constructive feedback, and useful suggestions.
* **On the number of labeling functions**: We discussed the relationship between the number of LFs, fairness, and performance in our common response. The result shows that our method can resolve u... | Rebuttal 1:
Rebuttal: We thank all of the reviewers for their kind comments and feedback. Reviewers recognized the strengths of our paper, which we briefly reiterate before we dive into in-depth responses.
* **This is the first study of fairness in weak supervision** (Reviewers wmSM, UYjh, GLUc, 6e6W).
* Our approach o... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices | Accept (poster) | Summary: The paper focuses on enabling federated learning (FL) among clients that are bounded by the memory capacity. To this end, the work proposes successive layer training (SLT), a technique that sustains the memory usage below a given memory budget throughout the FL process. SLT partitions the model into three part... | Rebuttal 1:
Rebuttal: We like to thank the reviewer for the fair and constructive feedback on our manuscript. We will consider changing the papers title.
# Performance of HeteroFL and FjORD
We think the performance of HeteroFL and FjORD (Section 3.4) is limited due to the same effect that is present in FedRolex and FD... | Summary: The paper proposes an FL training methodology to reduce the memory footprint for constrained devices. Specifically, the layers inside the original model are divided into three categories, i.e., frozen, fully trained, and partially trained. The frozen layers only participate in the forward pass but do not requi... | Rebuttal 1:
Rebuttal: We like to thank the reviewer for the fair and constructive feedback on our manuscript.
# SLT design choices ($K_T, K_F, s$)
Our general choice of setting $K_T = K_F + 1$ while maximizing $s$ throughout all configurations $N$ is motivated by preliminary ablation studies we conducted (please refer... | Summary: This paper introduces an approach known as Successive Layer Training (SLT) for federated learning (FL) on edge devices with limited resources. The core concept of SLT involves training a portion of the neural network (NN) parameters on the devices while keeping the remaining parameters fixed. Subsequently, the... | Rebuttal 1:
Rebuttal: We like to thank the reviewer for the fair and constructive feedback on our manuscript.
# Model capacity
## Model capacity in conducted experiments
The observation is correct that in some experiments the NN architecture is over-parametrized for the given dataset. We observe this, especially for t... | Summary: In this paper, authors study the case where the edge device is not capable of holding the training of the whole network due to limited memory. Authors find that it is necessary to train a whole network rather than train a submodule as previous methods do. Motivated by this observation, authors propose to first... | Rebuttal 1:
Rebuttal: We like to thank the reviewer for the fair and constructive feedback on our manuscript.
# SLT's improvements over a "small model" baseline
* The observation is correct that parts of the model are never trained end-to-end in SLT. However, SLT pretrains a subset of all layers end-to-end in step $0$... | Rebuttal 1:
Rebuttal: We attached a pdf with four figures (R.1, R.2, R.3, R.4) to support our answers to the reviewers' questions.
Pdf: /pdf/002383a2b13315dd71d11ced6e185a68f2fa7976.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposed a new training method for neural networks in the federated setting, called successive layer training. This method tries to train NN layer by layer by freezing the trained layer and scaling down the NN heads. The authors have a clear presentation for this method and propose detailed experime... | Rebuttal 1:
Rebuttal: We like to thank the reviewer for the fair and constructive feedback on our manuscript.
# Scope of the work
Our work is motivated by the observation that the current state of the art in memory-aware-FL through scaling of the NN does not work well. We think that the bad performance is caused by th... | null | null | null | null | null | null |
FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning | Accept (poster) | Summary: This paper presents FedGame, a game-theory based dynamic defense mechanism against backdoor attacks in Federated Learning (FL). FedGame adapts to attackers using a minimax game model. The authors theoretically prove that a model trained with FedGame is close to one trained without attacks, implying robustness ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments.
**C1: Any assumption on the auxiliary global model and global model.**
Thanks for the insightful question. Our auxiliary global model is obtained by updating the global model in the previous FL round with the average local model updates of th... | Summary: Existing defenses consider a static attack model where an attacker sticks to a fixed strategy and does not adapt its attack strategies, causing they are less effective under adaptive attacks. Hence, the authors propose FedGame to bridge this gap. The main idea of FedGame is to model single or multi-stage strat... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments.
**C1: Large-size trigger.**
We note that our framework is very general and is compatible with any trigger reverse engineering methods such as NC and FeatureRE. For instance, FeatureRE [42] (published in NeurIPS’22) shows that it is effective f... | Summary: Federated learning (FL) enables a distributed training paradigm, where multiple clients can jointly train a global model without needing to share their local data. However, recent studies have shown that federated learning provides an additional surface for backdoor attacks. For instance, an attacker can compr... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments.
**C1: Have you cited the latest and relevant research results?**
Thanks for the question. To the best of our knowledge, we have cited those research results. We will do a comprehensive literature survey to add and discuss more recent research ... | Summary: The authors introduce a game-theoretical defense mechanism named "FedGame" designed to safeguard against backdoor attacks in the Federated Learning (FL) environment. They demonstrate through theoretical analysis that training the global FL model using FedGame under backdoor attacks yields results closely resem... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable suggestions.
**C1: Why the game is multi-stage.**
We are sorry for the confusion. In the abstract, we meant to say a multi-stage interaction between the attacker and defender over different FL rounds instead of a multi-stage game.
**C2: Minmax term not cle... | Rebuttal 1:
Rebuttal: We really appreciate the reviewers for the constructive feedback and suggestions. We are glad that the reviewers find our work novel, effective, and well-organized. In summary, we have added the following additional experiments following the reviewers’ suggestions:
1. We add an experiment to vali... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
RegBN: Batch Normalization of Multimodal Data with Regularization | Accept (poster) | Summary: This paper focuses on mitigate the influence of unwanted variability and bias in multimodal data. To this end, the authors proposed a novel non-parametric regularization technique, i.e., RegBN. The proposed method utilizing Frobenius norm to constrain cross modality consistency, enabling model to discern unde... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for the comments and feedback. We have addressed each comment carefully and provided a point-by-point response, as outlined below.
*Question #1: Method: The proposed method mitigates the influence of unwanted variability and bias by minimizes the linear re... | Summary: This paper introduces an approach for the normalization of multimodal data called RegBN. The RegBN aligns multimodal features by a learnable projection matrix with regularization in the form of the Frobenius norm. The regularization strength is updated with the L-BFGS optimization algorithm. Extensive experime... | Rebuttal 1:
Rebuttal: We appreciate the comments and feedback provided by the reviewer. We would like to address the concerns raised regarding the presentation of our method in the manuscript. We reordered the comments to ensure an efficient and comprehensive response, taking into account overlapping concerns.
*Comme... | Summary: The paper introduces a novel approach called RegBN for normalizing multimodal data before fusion in neural networks. The integration of heterogeneous multimodal data poses challenges due to confounding effects and dependencies among different data sources, which can introduce variability and bias. RegBN addres... | Rebuttal 1:
Rebuttal: We extend our gratitude for the reviewer's comments and insightful suggestions. Below, we have provided a point-by-point response to each comment, encompassing all concerns, for your careful consideration.
*Comment: Authors conducted experiments between with/without RegBN and also compared with o... | Summary: This paper proposes RegBN for the normalization of multimodal data. RegBN uses the Frobenius norm as a regularizer term to address the side effects of confounders and underlying dependencies among different sources of data. The proposed method generalizes well across multiple modalities and eliminates the need... | Rebuttal 1:
Rebuttal: We express our gratitude for the reviewer's comments and feedback on our study. We have taken into account all the comments and kindly request the reviewer to consider our responses, which we have addressed in a meticulous manner.
*Comment #1: My main concern is that the proposed method lacks com... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their insightful and constructive feedback. We have incorporated their suggestions and responded comprehensively to their comments. Since the reviewer comments do not share common themes, we have addressed them individually. We have incorporated a Q&A section i... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper presents a novel regularization approach tailored for multimodal/heterogenous data fusion, which can be applied off-the-shelf to modern neural network architectures. The approach, namely RegBN, targets the reduction/removal of confounding factors and partial dependencies that are usually present amon... | Rebuttal 1:
Rebuttal: We express our sincere appreciation to the reviewer for providing valuable comments and suggestions. We have provided a point-by-point response, as outlined below.
*C#1 & Q#1:
C) No integration with recent widely adopted multimodal foundation models,...
Q) Do the Authors envision any use/applica... | null | null | null | null | null | null |
Robust Matrix Sensing in the Semi-Random Model | Accept (poster) | Summary: This paper studies the problem of low-rank matrix recovery for semi-random measurement matrices. This model allows for a mix of RIP matrices and arbitrarily chosen matrices. Using an iterative re-weighted approach, the authors demonstrate provable recovery using concepts from sparse recovery.
Strengths: The p... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and positive feedback.
On “combinations of sparse and low-rank matrices”: If we understand correctly, the reviewer is referring to the problem of sensing a matrix M in the semi-random model, in which the underlying matrix M can be decomposed as M ... | Summary: This paper study the matrix sensing problem (or low-rank matrix recovery) in the so-called semi-random model. The semi-random model stems from the fact that an unknown set of sensing matrices satisfy the Restricted Isometry Properties (RIP) while the remaining set are chosen adversarily. This model has the adv... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and positive feedback.
Regarding bad local optima in the semi-random model (Q1, Part 2): Section 5 of [BNS16] proved that bad local minima exist if the sensing matrices do not satisfy RIP. We mentioned this in Lines 74-77 of our paper. More precis... | Summary: This paper studies the matrix sensing problem in the semi-random model, where a subset of the matrices satisfy an RIP property and the rest are adversarially chosen so as to make the RIP not hold. The algorithm iteratively updates weights on the sensing matrices throughout the iterations to search for the set ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and positive feedback.
On the challenges in the matrix case: While the generalization from vectors to matrices (by considering the singular values as a “vector”) may appear intuitive, such generalization should not be taken for granted. To illustr... | Summary: The paper introduces a new descent algorithm for low rank matrix recovery in the setting of the semirandom model (where a subset of the measurements satisfy the RIP). The algorithm is based on a research of the best reweighting of the measurements (in order to navigate the non convex landscape) and a minimizat... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and positive feedback.
Regarding how the non-RIP matrices are generated and why they may cause issues: The non-RIP matrices can be arbitrary. In fact, a computationally-unbounded adversary can generate (any number of) additional sensing matrices $... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstruction | Accept (poster) | Summary: The paper proposes a zero-shot method for non-rigid shape matching based on
unsupervised functional map regularisation and non-rigid shape deformation regularisation.
The proposed method combines both intrinsic information from the functional map regularisation and
the extrinsic information from shape deforma... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and thoughtful comments and suggestions. We invite the reviewer to consider the general feedback that we provided to all reviewers in the "Author Rebuttal" answer. Below are our answers to the reviewers’ questions.
**Q1: Difference between the proposed m... | Summary: A zero shot method for computing correspondences between meshes is proposed. The core idea is to use DiffusionNet to produce features which are then used to produce a functional map from which a p2p mapping is produced. Using it, a deformation module using Primo is employed to deform the meshes to one another.... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and thoughtful comments and suggestions. We invite the reviewer to consider the general feedback that we provided to all reviewers in the "Author Rebuttal" answer. Below are our answers to the reviewer's questions.
**Q1: The Use of "Zero-Shot"**
Thank you... | Summary: The paper proposes a novel zero-shot method for non-rigid shape matching that eliminates the need for extensive training or ground truth data. The proposed method, Shape Non-rigid Kinematics (SNK), operates on a single pair of shapes and employs a reconstruction-based strategy using an encoder-decoder architec... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and thoughtful comments and suggestions. We invite the reviewer to consider the general feedback that we provided to all reviewers in the "Author Rebuttal" answer. Below are our answers to the reviewer's questions.
**Q1: Enhancing Comparisons with Related ... | Summary: This paper works on learning a point-to-point mapping between two sets of deformable mesh vertices in a self-supervised manner. To this end, the authors extract features of two meshes from a DiffusionNet, solve for functional maps by an optimization problem, and finally convert the functional maps to a point-t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and thoughtful comments and suggestions. We invite the reviewer to consider the general feedback that we provided to all reviewers in the "Author Rebuttal" answer. Below are our answers to the reviewer's questions.
**Q1: Further Insights on the Feature Ex... | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive comments. We find the suggestions to be beneficial for improving the quality of our work, making it clearer and more convincing.
Before responding to individual concerns, we stress the following contributions of our work:
* We have devised a new dec... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Differentiable Clustering with Perturbed Spanning Forests | Accept (poster) | Summary: This paper proposes a differentiable clustering method based on a stochastic perturbation of mnimum-weight spanning forests. They demonstrate its performance on several datasets in supervised and semi-supervised settings.
Strengths: This paper introduces a differentiable clustering method based on linear prog... | Rebuttal 1:
Rebuttal: Thank you very much for your positive evaluation of our work, as well as the interesting points that you raise. We have taken this opportunity to address them in the following manner.
### *Elaborating on the setting*
We have added a remark clarifying how $M_\Omega$ is given, from label informati... | Summary: The paper presents a novel method for differentiable clustering in combination with a novel loss derived from Fenchel-Young losses.
Strengths: The paper proposes a new concept for differentiable clustering.
Differentiable clustering has been a topic with only few applicable methods so far, so it is great to s... | Rebuttal 1:
Rebuttal: Thank you very much for your positive evaluation of our work, as well as the interesting points that you raise. We have taken this opportunity to address them in the following manner.
### *Scaling to more complicated datasets*
Thank you very much for your suggestion, and the indication that you... | Summary: This paper introduces a differentiable clustering method based on minimum-weight spanning forests.
This method can be integrated into end-to-end trainable pipelines and handle datasets with high noise and challenging geometries.
The key idea is to smooth the combinatorial clustering operation by taking expecta... | Rebuttal 1:
Rebuttal: Thank you very much for your positive evaluation of our work, as well as the interesting points that you raise. We have taken this opportunity to address them in the following manner.
### *Scaling to more complicated datasets*
As our method can use mini-batches, it is suitable for training deep ... | Summary: The paper presents a differentiable clustering approach based on k-spanning forests. It formulates the clustering assignment as an NxN membership matrix while introducing partial Fenchel-Young losses to optimize the between the cluster constraint and similarity/affinity matrix. The study demonstrates the appli... | Rebuttal 1:
Rebuttal: Thank you very much for your positive evaluation of our work, as well as the interesting points that you raise. We have taken this opportunity to address them in the following manner.
### *Comparison with other clustering algorithms*
We have added a quantitative comparison for different clusteri... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers and the area chair for their overall positive evaluation of our work.
We are thankful for the high-quality, informative reviews, and their constructive suggestions. We have made the following changes, further described in individual replies to reviewers:
- W... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Interpretability at Scale: Identifying Causal Mechanisms in Alpaca | Accept (poster) | Summary: This paper introduces an advancement of Distributed Alignment Search (DAS), termed Boundless DAS, where the brute-force search steps are replaced with learned parameters. This enhancement enables efficient exploration of interpretable causal structures in large language models and therefore results in a scalab... | Rebuttal 1:
Rebuttal: *Thanks* for your insightful comments and it **leads us to picture more clearly the improvement we have done compared to DAS [Geiger et. al., 2023] and why is that important**. We further clarify our main contributions besides simply proposing a new method in finding alignments. Here, we address a... | Summary: Finding alignments between variables in a user-hypothesized causal model for how a task could (or should) be performed and representations in neural networks, termed "causal abstraction", is a mechanistic interpretability technique for understanding how neural networks develop predictions at a granular level b... | Rebuttal 1:
Rebuttal: *Thanks* for the useful suggestions. **They lead us to define more clearly how to interpret our results, and existing limitations** of Boundless DAS.
> **Q1:** "The paper does not read independently (particularly section 3.1) to a reader unfamiliar with prior work.”
**A1:** We plan to use the e... | Summary: This paper builds on Distributed Alignment Search by introducing a more efficient learned model to search for a given circuit in a model. They test this method on a specific task: asking the model whether a given numeral falls between two other given numerals. They have a single task, and find an alignment wi... | Rebuttal 1:
Rebuttal: We *greatly appreciate* the reviewer’s in-depth feedback.
> **Q1:** "how can you ensure that Boundless DAS is behaving interpretably? ... failing to find an alignment does not mean that the alignment doesn’t exist."
**A1:** These are **incisive questions that are worthy of broader discussion**... | Summary: This work proposes an extension to Distributed Alignment Search (DAS) called "Boundless DAS" that replace the brute-force search with learned parameters in order to scale the method to large language models (LLMs).
The method evaluated on the Alpaca 7B model tasked to output "yes" or "no" to a simple numerical... | Rebuttal 1:
Rebuttal: We *thank* the reviewer's *thoughtful comments*. We **address the major concern around the applicability of Boundless DAS as well as how to interpret alignment results** where multiple causal models reach good interchange intervention accuracy (IIA). Here, we address all concerns with point-by-poi... | Rebuttal 1:
Rebuttal: We thank the reviewers for their incisive comments and questions. In this comment, we summarize our overall response to major points, emphasizing new experiments. This is followed by point-by-point responses.
1. **On the question of novelty and utility**, we note that Boundless DAS is the only me... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces a method called Boundless DAS, an algorithm that can automatically test the "alignment" between human-specified symbolic algorithms and the computational structure found in the weights of a neural network. The authors apply Boundless DAS to a real-world 7B parameter language model called ... | Rebuttal 1:
Rebuttal: We *thank* the reviewer's *inspiring comments about future works* that can be built on top of Boundless DAS. We **provide more discussions about possible future extensions**, and we will revise our paper to reflect these additional thoughts. Here, we address all concerns with point-by-point respon... | Summary: This paper proposes a method for interpreting neural models by learning an alignment of their representations to a specified interpretable causal model. The method extends prior work, namely Distributed Alignment Search (DAS), by addressing a key limitation that prevents the scaling of DAS. The limitation is o... | Rebuttal 1:
Rebuttal: We *thank* the reviewer's *great suggestions on detailing improvements* over DAS and **highlights the scalability that our method brings**. We will use the additional page to clarify our improvements and provide a detailed introduction to the DAS method [Geiger et. al., 2023]. Here, we address all... | null | null | null | null |
Multiclass Boosting: Simple and Intuitive Weak Learning Criteria | Accept (poster) | Summary: This paper tackles the problem of multi-class boosting by proposing a novel definition for weak-learning based on list boosting. The authors define a weak-learning condition for multi-class learning based on the simple principle of slightly better than random guessing. This definition is then used as a steppin... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and suggestions.
- “comparison between … existing ones” - The reviewer implies that we have not included a comparison to prior works, yet all of the works they mention are explicitly discussed in the paper. Addboost.MR is mentioned in line 130, and is a ra... | Summary: Boosting algorithms is an important fundamental question in SLT starting with celebrated AdaBoost algorithm. While the original results consider binary setting and the condition on the weak learner is straightforward, it is not trivial to formulate the same result for classification with more than one classes.... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and suggestions.
- “Theorem 1 ” Notice that in our framework (in contrast to previous works on multiclass boosting) realizability assumption is not needed, and the empirical BRG assumption suffices to get the result. The reason is that by using compression... | Summary: The paper proposes a novel treatment of traditional (non-gradient) boosting to multiclass prediction problems. In particular it shows that a novel relaxation of the weak learning criterion allows the definition of boosting algorithm with the usual success guarantee of reducing the empirical misclassification r... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and suggestions.
- “Papers on classical boosting … narrow” - Gradient boosting methods are valuable algorithmic tools but lack a theoretical framework for studying the task of interest: multiclass classification, with respect to classification loss. Unlike... | Summary: This paper proposes a generalization of boosting to the multiclass setting. To this end, the authors introduce a new weak learning assumption for multiclass based on a ‘hint’, which takes the form of a list of $k$ labels, named ‘Better-than-Random Guess’ (BRG). the authors present a main boosting method and pr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and suggestions.
- “lack of experiments” - we emphasize that the main contribution of this work is the theoretical framework extending classical boosting theory to the multiclass setting. The algorithmic contribution is not the main focus of the paper. The ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Eliminating Domain Bias for Federated Learning in Representation Space | Accept (poster) | Summary: This paper addresses the significant challenge of statistical heterogeneity among clients in federated learning. To tackle this issue, the authors propose personalized federated learning, where each client can learn and apply its specific model parameters. Specifically, the authors introduce DBE (Domain Bias E... | Rebuttal 1:
Rebuttal: Thank you for your time and constructive comments. We respond to your concerns as follows in the form of "**[A weakness or question]** Our responses".
**[Improve the presentation of the memory module]** Thank you once again for your valuable suggestions. We will further refine the presentation ... | Summary: This paper tackles the problem of representation bias phenomenon lead by biased domains and proposes Domain Bias Eliminator (DBE), a framework that reduces domain discrepancy between server and client in representation space by Personalized Representation Bias Memory (PRBM) and Mean Regularization (MR). Experi... | Rebuttal 1:
Rebuttal: Thank you for your time and insightful feedback. We respond to your concerns as follows in the form of "**[A weakness or question]** Our responses".
**[Evaluate methods on real-world distribution shifts]** Our $\texttt{DBE}$ is also effective in real-world scenarios. Here, we evaluate FedAvg+$\... | Summary: To address the performance drop via heterogeneous data distribution, the proposed method - Domain Bias Estimator (DBE) - decomposes image features into unbiased (global) and biased (personalized) representations.
To guide the feature extractor to obtain the unbiased representation, Mean Regularization (MR) i... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and insightful comments. We respond to your concerns as follows in the form of "**[A weakness or question]** Our responses".
**[Privacy issues for client-specific mean collection (*line 2,3* in Algorithm 1, Supplementary)]** Please note that the client-speci... | Summary: The paper introduces Domain Bias Eliminator (DBE) to address the representation bias and representation degeneration issues commonly observed in federated learning under statistically heterogeneous scenarios. DBE utilizes a Personalized Representation Bias Memory (PRBM) to preserve representation bias and mean... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and insightful comments. We respond to your concerns as follows in the form of "**[A weakness or question]** Our responses".
**[More detailed explanation on how $\texttt{DBE}$ reduces the domain discrepancy]** As mentioned in *Section 4.4 Improved Bi-dir... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their valuable feedback on our manuscript. We have provided detailed responses in the rebuttal field following each reviewer’s comments. In this global response field, we provide a PDF file that includes a figure named R-8bi2-Figure 1.
Pdf: /pdf/6f49535dd572641... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Memory Efficient Optimizers with 4-bit States | Accept (spotlight) | Summary: This paper concentrates on quantizing the optimizer states into low-bit to reduce training memory consumption. By thoroughly analyzing the first and second momentums, the authors quantize the optimizer states to 4-bit via the use of a smaller block size and both row-wise and column-wise information. They perfo... | Rebuttal 1:
Rebuttal: We thank reviewer f2sV for the constructive comments. With respect to your questions:
**Weakness: Common sense reasonging and MMLU evaluations on large models.**
We have fine-tuned LLaMA-7B and LLaMA-13B with 32-bit AdamW and 4-bit AdamW on Alpaca and evaluated them on common sense reasoning task... | Summary: Today’s machine learning pipeline places a heavy load on GPU memory during training, due to the large number of parameters the optimizers have to maintain during training. This paper proposes a method to reduce the internal states of optimizers from their full-width counterparts to 4-bit numbers, while maintai... | Rebuttal 1:
Rebuttal: We thank reviewer d65g for the careful review. With respect to the figures, we will use larger ones in a later revision.
With respect to your questions:
**Weakness + Question 1: How to pick the block size.**
There are some rules to choose the block size:
- Firstly, quantizing with a smaller b... | Summary: The paper develops 4-bit optimizers by using a smaller blocksize for the first moment and by analyzing and finding solutions to outlier patterns in the second moment. In particular it is found that quantization to the zeropoint are problematic for the second moment. The analysis is extensive and robust leading... | Rebuttal 1:
Rebuttal: We thank reviewer qs7T for the valuable and careful review.
Consider a weight matrix $x$ of size $m \times n$. In Rank-1 normalization, we store the absmax of each row and the absmax of each column during the quantization stage, totaling $m + n$ elements.
In the dequantization stage, we calculate... | Summary: This paper proposes a 4-bit optimizer to save the memory of model training. They use a smaller block size and propose to utilize both row-wise and column-wise information for better quantization. They identify a zero-point problem of quantizing the second-order momentum and solve this problem with a linear qua... | Rebuttal 1:
Rebuttal: We thank reviewer rPDD for the constructive review. With respect to your questions:
**Weakness 1 + Question 1: Experiments on large models.**
We have fine-tuned LLaMA-7B and LLaMA-13B with 32-bit AdamW and 4-bit AdamW on Alpaca and evaluated them on common sense reasoning tasks and MMLU. Results ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks | Accept (poster) | Summary: This paper proposes a novel pre-training framework (GPST) for ST prediction, it mainly integrates the ST parameter personalization scheme and the region-wise semantic association mechanism into the pre-training model, and conduct the training in an unsupervised manner. Besides, the proposed model GPST adopts t... | Rebuttal 1:
Rebuttal: ***Response to Reviewer 83m7***
**Comment 1:** Further discussion on the design of the pre-training stage and data distribution.
**Response:** Thank you for your feedback. This work primarily introduces a spatio-temporal pre-training framework to enhance downstream baselines, where the effecti... | Summary: This paper introduce pre-training task for spatio-temporal. The algorithm design includes a spatial-temporal knowledge extractor and a pre-training mechanism.
The design of the pre-training methods is to resolve the two limitations of existing spatial-temporal learning: (1) lack of comprehensive personalizati... | Rebuttal 1:
Rebuttal: ***Response to Reviewer 8n4n***
**Comment 1:** The difference between our pre-training method and the static graph approach.
**Response:** Thank you for your suggestion. Compared to static graphs, spatio-temporal (ST) graphs focus more on uncovering the temporal evolution patterns of node feat... | Summary: The authors introduce a spatio-temporal pre-training framework that can be easily integrated into downstream baselines. The framework comprises personalized parameter learners and hierarchical hypergraph networks. The former enables the acquisition of spatio-temporal personalized representation and the latter ... | Rebuttal 1:
Rebuttal: ***Response to Reviewer fFpq***
**Comment 1:** Clarifications of notations and formulas.
**Response:** We sincerely apologize for any confusion that may have arisen regarding the notations and equations, and greatly appreciate your feedback. In order to enhance the clarity of our work, we will... | Summary: This paper introduces a spatio-temporal pre-training framework called GPST (Generative Pre-training framework for Spatial Temporal prediction) to enhance the performance of existing models in traffic management and travel planning. The goal is to address the challenges of integrating and expanding refined mode... | Rebuttal 1:
Rebuttal: ***Response to Reviewer H5wT***
**Comment 1:** The inclusion of the suggested related work.
**Response:** Thank you for your suggestion. The self-supervised learning methods for graph networks and multivariate time series forecasting will be incorporated into the related work in our revised ve... | Rebuttal 1:
Rebuttal: ***Responses Regarding Literature Review***
We extend our heartfelt gratitude to the reviewers for their valuable time and effort dedicated to evaluating our work. Due to constraints on character count, we will address the discussion of all relevant works mentioned by the reviewers in this secti... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Provably Efficient Algorithm for Nonstationary Low-Rank MDPs | Accept (poster) | Summary: The paper presents a new way to perform off-policy RL coupled with representation learning over non-stationary low-rank MDPs, both in a parameter-based and a parameter-free fashion. The paper proposes some interesting new algorithmic techniques to tackle this (off-policy
exploration, data-transfer model learni... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing the helpful review! Below we address the reviewer’s comments.
Q: My only comment is about the exposition of the algorithm. I would suggest not citing equations but reporting them, and providing the output of the subroutines.
A: Thanks for the suggestions. We w... | Summary: This paper present an algorithm for solving non-stationary low-rank MDP. It also provide a theoretical analysis of their algorithm,
Strengths: This is an interesting question. The presentation is clear.
Weaknesses: I sketch through the results and the proof. I doubt that the proof might be wrong. In page 14... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing the helpful review! Below we address the reviewer’s comments.
Q1: I sketch through the results and the proof. I doubt that the proof might be wrong. In page 14, the inequality (i) require the author to provide an upper bound of $E_{P^*,{\pi^{\star,k}}} f_h^k - ... | Summary: This paper investigates nonstationary RL in the context of episodic low-rank MDPs, where both transition kerns and rewards may change from round to round, and where the low-rank model contains unknown representations. The goal of this work is then to develop methods with low average dynamic suboptimality gap, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing the helpful review! Below we address the reviewer’s comments, and we will revise the paper as suggested. If our response resolves your concerns to a satisfactory level, we kindly ask the reviewer to consider possibly raising the score of your evaluation.
Q1: P... | Summary: This paper studies reinforcement learning (RL) under non-stationary MDPs where the state transition and reward function changes across episodes. The paper focus on the low-rank MDP setting, in which the state transition function admits a low-rank decomposition. The authors propose a novel algorithm, namely POR... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing helpful reviews! Below we address the reviewer’s comments, and we will revise the paper as suggested. If our response resolves your concerns to a satisfactory level, we kindly ask the reviewer to consider possibly raising the score of your evaluation.
Q1: Ind... | Rebuttal 1:
Rebuttal: Dear all reviewers,
We thank all the reviewers for providing the helpful review comments!
We further thank Reviewers niTs, FzWn and 5jBS for your positive evaluation of our paper. We have responded to your specific questions and will revise our paper based on your comments.
We also thank Revi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Framework for Fast and Stable Representations of Multiparameter Persistent Homology Decompositions | Accept (poster) | Summary: Persistent homology (PH) is the most important method in topological data analysis (TDA), and multiparameter persistence (MPH) is its natural generalization which is expected to significantly boost its performance. However, because of several mathematical roadblocks, MPH could not be effectively used in real l... | Rebuttal 1:
Rebuttal: *Experiments section could be more detailed, and more in-depth analysis could have been provided for the performance of these vectorizations in real life applications. In particular, the authors compare the performance of their vectorizations only with respect to other TDA models, and some simple... | Summary: The paper investigated the persistent homology with multiple filtration parameters. The authors proposed a framework, T-CDR, which generalized the previous studies in multi-parameter persistent homology. They further presented stability and convergence guarantees on S-CDR, which is a special case of T-CDR intr... | Rebuttal 1:
Rebuttal: *In the empirical convergence rate experiment, [...] How do you get the for the immunohistochemistry data (as in Figure 4)?*
This is a very good question. As you mentioned, we do not know the true distribution, so the target that we use (and that we measure the distance to) is the S-CDR obtained... | Summary: As the title clearly suggests, a general representation of multiparameter persistent homology (MPH) is introduced. Unlike earlier representations that either yield a loss in information or are unstable, the proposed vectorization is strictly more informative, and is shown to be stable for a specific settings o... | Rebuttal 1:
Rebuttal: *The main experimental results in Table 1 show that the proposed S-CDR approach is half of the time significantly outperformed by other existing methods.*
This is correct. The table indicates that for some time series and graph tasks, topological methods are less effective than "classical" metho... | Summary: The paper addresses topological data analysis in the multiparameter setting and focuses on the problem of representation of multiparameter persistent homology by vector space elements so that standard ML algorithms can be applied. The key advance in the paper is the leveraging of decomposition ideas in multip... | Rebuttal 1:
Rebuttal: *Experiments on more complex datasets, e.g., imaging related applications. The experimental validation is limited but sufficient*
We have included a second set of experiments on graph data that demonstrates that our representations can be successfully applied to other types of data sets. Results ... | Rebuttal 1:
Rebuttal: We first want to thank all reviewers for their careful and insightful reviews. We have responded to their comments, and will be happy to provide further explanations if needed.
We have also prepared a rebuttal pdf that contains a few more experiments that were rightfully suggested by the reviewer... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts | Accept (poster) | Summary: This paper proposes a benchmark for evaluating model robustness against structural distributional shifts. The authors propose three different metrics to split the datasets for the evaluation. In the experiments, the authors evaluate different models under the proposed distributional shifts and show that they c... | Rebuttal 1:
Rebuttal: Thanks for your review! We appreciate that you also consider the problem of evaluating robustness and uncertainty of graph models under structural distributional shift to be understudied, and that you find our idea to use structural node properties for inducing distributional shifts novel. Below, ... | Summary: The authors propose three domain selections, namely, popularity, locality, and density to equip node prediction datasets with structural distribution shifts. Extensive experiments are conducted to compare present OOD generalization methods and OOD detection methods.
Strengths: S1: The proposed distribution sh... | Rebuttal 1:
Rebuttal: Thanks for your review! We appreciate your encouraging feedback about our work and address your comments below.
W1. Thanks for this remark. Indeed, the notion of structural distributional shift might be significantly different in graph-level problems, so it is important to emphasize that our appr... | Summary: This paper provides a benchmark solution for producing diverse and meaningful distributional shifts from existing graph datasets. The experiments conducted demonstrate that the proposed distributional shifts can be difficult for existing graph models, and surprisingly, simpler models often outperform more soph... | Rebuttal 1:
Rebuttal: Thanks for your review! We appreciate that you find our approach to create data splits interesting and the presentation easy to follow. Below, we reply to your comments.
W1. Our main contribution is an approach for creating data splits with distributional shifts based on various structural proper... | Summary: This paper proposes a general approach for inducing diverse distributional shifts based on graph structure and evaluates the robustness and uncertainty of graph models under these shifts. The authors define several types of distributional shifts based on graph characteristics, such as popularity and locality, ... | Rebuttal 1:
Rebuttal: Thanks for your review! We appreciate your support and address your comments below (the order of questions is preserved).
Q1. In graph-level problems, the notion of structural distributional shift might be significantly different and depend on particular applications. On the contrary, many node-l... | Rebuttal 1:
Rebuttal: **General response**
We would like to thank all the reviewers for their valuable feedback and suggestions! In this general response, we address questions raised by several reviewers and describe additional experiments we conducted.
**Additional experiments**
As requested by Reviewer 2ynW, we ex... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
PromptRestorer: A Prompting Image Restoration Method with Degradation Perception | Accept (poster) | Summary: This paper introduces the PromptRestorer, a method for image restoration that effectively utilizes raw degradation features to guide the restoration process. PromptRestorer consists of two branches: a restoration branch and a prompting branch. The restoration branch restores the images, while the prompting bra... | Rebuttal 1:
Rebuttal: Q: What is a more accurate degradation prior? Can we measure it?
A: The most accurate degradation priors should be the degraded image itself. In feature space, better degradation should have the property that can better represent the features of the degraded image. We note that VQGAN can reconst... | Summary: In this paper, the authors find that the degradation vanishes in the learning process of the image restoration problem. Which further hinders the model's capacity. To solve this problem, this paper proposes the PromptRestorer, a prompting image restoration method by exploring the raw degradation features extra... | Rebuttal 1:
Rebuttal: Q: Although this paper clarifies many strengths of the method, what’s the limitation? The author should present it in this paper.
A: Since our method needs to utilize a pre-trained model to extract degraded priors, it consumes more parameters and FLOPs compared with the model without any conditi... | Summary: This paper proposes the PromptRestorer by taking advantage of the prompting learning for image restoration. The author considers raw degradation features into restoration which consistently retains the degradation priors to facilitate better restoration. To better perceive degradation, the authors propose Glob... | Rebuttal 1:
Rebuttal: Q: The authors can give a clearer understanding of the degradation priors guidance. Why does it work better than the model with a learnable conditional branch?
A: The learnable nature of the conditional network in these models does not effectively provide degradation information for the restorati... | Summary: This paper proposes a prompting image restoration method with degradation perception. The authors show that raw degradation features can effectively guide deep restoration models, providing accurate degradation priors to facilitate better restoration. To perceive the degradation, the authors propose prompting ... | Rebuttal 1:
Rebuttal: Q: The author claims that the pre-trained model is the same as the learnable conditional network. Hence, the FLOPs in Tab. 5 should be the same.
A: Thanks for your reminder. The reported FLOPs of Case 3 in Tab. 5 are the restoration network. We will correct it in the revised paper.
Q: The author... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes PromptRestorer for image restoration, addressing the issue of degradation vanishing in existing methods. Unlike previous approaches that do not explicitly consider degradation information or fail to effectively model degradation priors, PromptRestorer leverages the raw degraded features extr... | Rebuttal 1:
Rebuttal: Q: The main problem ''degradation vanishing'' is not well defined and discussed in this paper, leading to a confusing and distracting presentation.
A: ''Degradation vanishing'' means the degradation features gradually vanish to become sharper features in the learning process of restoration networ... | Summary: This paper proposes a Prompt image Restorer, which contains a restoration branch and a prompting branch. A pre-trained model is utilized to extract features in the prompting branch and a prompting modulator is proposed to better perceive features from global and local perspectives in the restoration branch. Ex... | Rebuttal 1:
Rebuttal: Q: The prompting way proposed in this paper is similar to the approach taken in some other papers, such as "Take a Prior from Other Tasks for Severe Blur Removal", where features are added as conditions. PromptRestorer doesn't have a significant difference.
A: The abovementioned method uses a sem... | null | null | null | null |
$H$-Consistency Bounds: Characterization and Extensions | Accept (poster) | Summary: This work proposes a general characterization and an extension of H-consistency bounds for multiclass classification. By introducing an error transformation function, the paper provides a general tool for deriving H-consistency bounds with tightness guarantees. The paper demonstrates that the proposed tools an... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work. Below please find responses to specific questions.
**Could you provide further explanation on the connection between the results presented in this study and the theory of calibration functions as described by Steinwart in 2007?**
**Response:** The cal... | Summary: Unlike previous work on H-consistency bounds for surrogate loss functions, this paper attempts to analyze them from a more unified perspective.
For this purpose, the paper proposed a new error transformation method to present new tight H-consistency bounds for comp-sum losses and constrained losses in multi-cl... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review. We have carefully addressed all the questions raised. Please find our responses below.
**Weaknesses:**
**1. I think the argument of section 2 should be improved to understand more easily.**
**Response:** We thank the reviewer for their suggestions. We will... | Summary: The authors propose a general characterization and extension of H-consistency bounds for multi-class classification. They introduce an error transformation function that serves as a general tool for deriving these guarantees with tightness guarantees. The paper demonstrates that calculating the error transform... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work. Below please find responses to specific questions.
**Can the theory be extended to derive tight bounds for more complicated models such as simple neural networks?**
**Response:** Good question! Our $H$-consistency bounds, as demonstrated in Theorem 2,... | Summary: H-consistency bounds for surrogate loss function plays an important part in learning theory.
This manuscript provides a comprehensive characterization and extension of H-consistency
bounds for multi-class classification. The authors introduce a novel error transformation function
that enables the derivation of... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review. We have carefully addressed all the questions raised. Please find our responses below.
**Weaknesses:**
**This manuscript includes a wealth of work but only limited technical innovations compared to [1].**
**Response:** Our work introduces a key technical in... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents an extension of previous works on $\mathcal{H}$-consistency bounds for multi-classification using a novel method. The strength of the paper lies in proposing a new method and tool that guarantees tight $\mathcal{H}$-consistency bounds for multi-classification.
Strengths: The paper propose... | Rebuttal 1:
Rebuttal: Thank you for your useful comments. We have carefully addressed all the questions raised. Please find our responses below.
**1. One weakness of the paper is the lack of clarity in the discussion of tightness guarantees. While the statement in lines 191-192 suggests the existence of a distribution... | Summary: The authors present some extension study on the H-consistency bounds for various loss functions. They also introduce an error transformation function that the authors claim could be a general tool in deriving H-consistency bound.
Strengths: The authors seem very well aware of the related work and have solid c... | Rebuttal 1:
Rebuttal: Thank you for your useful comments. We have carefully addressed all the questions raised. Please find our responses below.
**Weaknesses:**
**Maybe the authors can simplify the notation and presentation so as the paper can be consumed by more general audience.**
**Limitations:**
**A bit hard for ... | null | null | null | null |
What Truly Matters in Trajectory Prediction for Autonomous Driving? | Accept (poster) | Summary: This paper presents a timely and well-executed study on motion prediction, focusing on the relationship between the dominant evaluation paradigm (static, offline metrics) and the true objective of research in this domain (safer planning). The study uncovers several important and surprising findings: (1) static... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comprehensive comments! We kindly ask the reviewer to let us know if further clarification or information is needed.
>A very interesting finding that I suggest should be highlighted more is that in all closed-loop evaluation settings, the best among the prediction m... | Summary: The authors examine the discrepancy between trajectory prediction accuracy and driving performance in the task of autonomous driving when a fixed dataset is used and in the presence of multiple surrounding traffic participants (such as other vehicles). Specifically, they assert that this discrepancy arises fro... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments! We kindly ask the reviewer to let us know if further clarification or information is needed.
>The work does not propose a novel approach but is instead a survey of existing methods.
We do not provide a new approach, but instead a prediction evalu... | Summary: The paper provides an extensive study on the discrepancy between model prediction accuracy and driving performance. It explores two major factors: 1. The dynamics difference; and 2. The computational efficiency of predictors. Various methods are tested in two settings: 1. Fixed Number of Predictions; and 2. Fi... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful analysis and constructive feedback on our paper. Your insights have been valuable, and we are grateful for the time you spent evaluating our work. Below, we address your main concerns:
> While the two factors are well supported by the experiment results given, it is ... | Summary: This work attempts to look for evaluation metrics for trajectory prediction beyond the widely used ADE/FDE metrics. In particular, the authors focus on two overlooked aspects in prediction evaluation, which are: 1) the dynamics gap between the dataset and closed-loop driving scenarios; 2) the trade-off between... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments! We kindly ask the reviewer to let us know if further clarification is needed.
>I did not think the authors provide any new perspective or insightful solution. The value of closed-loop evaluation has been widely acknowledged in autonomous driving... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for your constructive feedback and recognition of this work. The reviewers found:
* The problem is “original” (R-Aygk) and “interesting” (R-1Qcn).
* The experiments are “well-done” (R-tS95, R-RCyQ, R-1Qcn) and “extensive” (R-tS95, R-RCyQ).
* The work provide... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In the article, the author discusses the limitations of the current open-loop trajectory evaluation method: 1. It lacks consideration of closed-loop interactions with the ego-planner 2. It overlooks computational timeliness. These two factors result in a misalignment between open-loop trajectory evaluations an... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful comments! We kindly ask the reviewer to let us know if further clarification is needed.
>The core Dynamic ADE/FDE metrics are not clear… It would be beneficial to include an illustration.
This is a great point for us to clarify more! We add a figure in the attached PDF... | null | null | null | null | null | null |
Regret-Optimal Model-Free Reinforcement Learning for Discounted MDPs with Short Burn-In Time | Accept (poster) | Summary: The paper proposes a nearly optimal model-free algorithm in discounted tabular MDPs which has a shorter burn-in time than previous works and provides valid theoretical guarantees.
Strengths: The paper is well motivated, trying to shorten the burn-in time and obtain a more sample-efficient model-free tabular ... | Rebuttal 1:
Rebuttal: We really appreciate you taking the time to provide this valuable review! Let us provide responses to your comments and questions as follows.
> If I must point out some weaknesses, I think that the proof contains too many instances where the author didn’t include brackets when utilizing the summ... | Summary: This paper studied infinite discounted tabular Markov decision process (MDP). The authors proposed a model-free algorithm: Q-SlowSwitch-Adv that achieves optimal regret with low burn-in cost, low space complexity and low computational cost. They introduce an innovative technique that changes the execution poli... | Rebuttal 1:
Rebuttal: We really appreciate you taking the time to provide this valuable review! Let us provide responses to your comments and questions as follows.
> The paper lacks empirical evidence to substantiate the theoretical claims and the efficacy of the proposed algorithm, especially for the 'low' burn-in c... | Summary: This paper studies discounted infinite-horizon MDPs. The authors propose an online algorithm that attains optimal (i.e., least) regret with finite-sample performance guarantees.
Strengths: Optimal regret & finite-sample performance guarantees
Weaknesses: Lack of numerical illustration, but not a deal-break.... | Rebuttal 1:
Rebuttal: We really thank you for taking the time to provide this valuable review as well as your approval of our paper! Let us provide responses to your questions as follows.
Thank you for raising the point about numerical experiments and illustration. Our original goal is to resolve a longstanding theor... | Summary: The paper provides a version of Q-learning algorithm with Bernstein bonuses and control variate that allows or the correct (in terms of the dependence upon state and action space sizes $S$ and $A$) second-order term in the regret bound. This allows for an algorithm with the provably shorter burn-in period (aga... | Rebuttal 1:
Rebuttal: We really appreciate you taking the time to provide this valuable review! We also thank the reviewer for suggesting submission to a journal. We submitted it to this conference because we believed our paper can be seen by more people here and thus maximize its impact and contribution to the communi... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper studies online learning in tabular infinite horizon MDPs, and proposes a model free algorithm that obtains (nearly) minimax optimal regret with space complexity $SA$, and such that the number of time steps needed to reach the minimax-optimality regime is $O(SA/(1-\gamma)^{13})$.
Prior art achieving m... | Rebuttal 1:
Rebuttal: We really appreciate you taking the time to provide this valuable review! Let us provide responses to your comments and questions as follows.
> It is hard to say this work directly improves [14] ...
> Is there a more formal treatment of the two regret definitions? It would be helpful to make a ... | null | null | null | null | null | null |
Label Correction of Crowdsourced Noisy Annotations with an Instance-Dependent Noise Transition Model | Accept (poster) | Summary: The authors formulate the noise transition model in a Bayesian framework and design a new label correction algorithm. The authors further formulate the label correction process as a hypothesis testing problem and propose a novel algorithm to infer the true label from the noisy annotations based on the pairwise... | Rebuttal 1:
Rebuttal: Thanks so much for your valuable comments on our paper. Below we address each question in detail.
1.As shown in Table 1 of our paper, the proposed method outperforms the baseline methods in most cases. We also display the results from Table 1 in Figure 1 of the PDF attachment, which may help demo... | Summary: The paper introduces a method for modelling input-dependent label noise for classification in the presence of multiple annotators. The generative process of annotation is modelled as a hierarchical Bayesian model in which the observed labels are noisy versions of the latent true labels that are corrupted throu... | Rebuttal 1:
Rebuttal: Thanks for reading our paper and providing meaningful feedback. Below we address each question in detail.
**Weaknesses**
1.The performance of the proposed method is relatively robust to the prior distribution as long as it satisfies the conditions listed in Appendix A.3. Specifically, for comput... | Summary: The paper deals with the noisy annotation problem with crowdsourcing data. Instead of traditional transition matrices, the paper proposed to learn an instance-dependent transition model that uses a Bayesian network with guaranteed consistent posterior to approximate the noise in labels. The paper then proposes... | Rebuttal 1:
Rebuttal: Thanks for assessing our paper and providing meaningful comments. Below we address each question in detail.
**Weaknesses**
1.Thanks for your question.
(1) As shown in Appendix A.1 and Appendix A.2, we assume that the underlying noise transition probability can be approximated by a sparse model... | Summary: This paper focuses on the problem of estimating annotator-specific instance-dependent transition matrices. The authors propose a solution based on a family of Bayesian estimators to approximate these matrices, supported by theoretical foundations. Through rigorous mathematical proofs, the authors demonstrate t... | Rebuttal 1:
Rebuttal: **Weaknesses**
1.Thanks so much for pointing this out. The assumptions A.1-A.4 are in Appendix A.2 and we will revise this error in the final version of our paper. We apologize for the misreference and thanks again for reading our paper carefully.
2.We have now added more baseline methods PartT ... | Rebuttal 1:
Rebuttal: Dear reviewers,
Thank you for providing feedback! We appreciate your comments and the time and effort you took to read, comprehend, and assess our paper. We value your comments and concerns regarding how to emphasize our motivations and clarify the assumptions more clearly (Reviewer i77Y, PGxM, w... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a new method for label correction of crowdsourced data via an instance-dependent noise transition model. It parameterizes the instance-dependent noise transition matrices by a Bayesian network. A theoretical analysis that guarantees the posterior consistency of noise transition matrices is ... | Rebuttal 1:
Rebuttal: Thank you for providing such a comprehensive review.
1.We appreciate your thoughtful remarks. We will include this background and an explanation of how our work differs from earlier works in our paper. Specifically, existing methods investigate the human annotation process and propose different ... | null | null | null | null | null | null |
Contrastive Training of Complex-Valued Autoencoders for Object Discovery | Accept (poster) | Summary: This paper improves the state-of-the-art in synchrony based object centric learning methods. The current SoTA is the Complex-valued AutoEncoder (CAE). But CAE is unable to handle even the Tetrominoes dataset. The paper presents CAE++ and CtCAE.
CAE++ = CAE + minor architectural improvements
CtCAE = CAE++ + C... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing valuable feedback on our work and for the highly positive comments on the strengths of our contributions. We are very happy to hear that the reviewer found the method easy to understand, the paper to be well written and the experiments thorough.
Below are our r... | Summary: CAEs hold potential to address several issues associated with slot-based representation. They may provide flexible object granularity, enable the extraction of part-whole hierarchies as needed, and provide faster training speeds. However, testing of the original CAE was confined to grayscale images and a small... | Rebuttal 1:
Rebuttal: We thank the reviewer for giving us valuable feedback and critiques on our manuscript. We’re happy to hear that the reviewer found our paper to be well-written and ablation studies on architectural design to be useful and simplifying. Below are our responses to your questions/comments:
*“... doe... | Summary: This paper presents a collection of small modifications of the Complex-valued AutoEncoder architecture leading to improved reconstruction/segmentation performance on simple RGB datasets; a contrastive learning objective is introduced to further improve the performance.
Strengths: - The paper's story is easy t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and suggestions to improve our work. We are happy that the reviewer found the manuscript easy to follow and the main ideas presented to be clearly motivated. Below are our responses to the comments/questions from the reviewer:
*“ … captions of figu... | Summary: This paper proposes several improvements over the Complex AutoEncoder (CAE) to scale it to more complex multi-object datasets. The CAE is a synchrony-based model that learns object-centric representations by injecting complex-valued activations into every layer of its architecture, but it is only applicable to... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing valuable feedback on our work. We’re glad that the reviewer found our paper well-written, the proposed contrastive loss well-motivated and an elegant solution to the problem and the experiments/ablation studies insightful.
Below are our responses:
*”main pro... | Rebuttal 1:
Rebuttal: ### General Response to all Reviewers
We have provided individual responses to each reviewer's questions/comments. We have not made any changes to the PDF of the paper to ensure the ease of referring to the line numbers, Figure/Table numbers as in the original version. We’ve provided additional r... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Private Distribution Learning with Public Data: The View from Sample Compression | Accept (spotlight) | Summary: This paper studies differentially private learnability of probability distributions.
The results put forward are very interesting!
For example, they show that the public-private learnability of a class of distributions is connected to the existence of sample compression schemes as well as to an intermediate no... | Rebuttal 1:
Rebuttal: Thank you for thoughtful comments on our work. We are happy to hear the reviewer finds our results and topic of study interesting. We also thank the reviewer for their nice suggestions and questions.
In what follows, we respond to specific points raised by the reviewer.
—---------------------
>... | Summary: The authors consider the problem of learning a distribution in a known class using private samples under pure DP, as well as a number of public samples drawn from the same distribution. Some distribution classes, such as Gaussians, are not learnable using a finite number of private samples, or even with a fini... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful comments on our work. We are glad the reviewer finds the problem we study to be relevant, and our results to have potential for impact.
In what follows, we address specific points raised by the reviewer.
—---------------------
> Inadequate discussion of proof techniqu... | Summary: Continuing a line of work recently initiated by Bie, Kamath, and Singhal (NeurIPS '22), this submission studies differentially private distribution learning (a.k.a. hypothesis selection) in the presence of public data. The main result is a collection of quantitative equivalences between this task and the probl... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful comments and suggestions. We are glad the reviewer finds that our work offers some nice perspectives on the public-private learning problem. Indeed, we acknowledge the limitations pointed out by the reviewer: (a) that our equivalence only holds for the pure DP setting ... | Summary: In public-private learning, an algorithm gets two sets of i.i.d. samples from an unknown distribution $q$ from a class $\cal{Q}$ (say, the first set of size $m$ and the second one of size $n$), but is required to preserve DP only with respect to the second set.
This research direction is about understanding w... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful comments and suggestions. We are glad that the reviewer finds the connections we present between public-private learning and other notions of learning interesting. Indeed, our investigation only answers questions regarding the statistical complexity of public-private l... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
FouriDown: Factoring Down-Sampling into Shuffling and Superposing | Accept (poster) | Summary: This paper introduces a novel down-sampling operator in the Fourier domain, specifically tailored to mitigate the common problem of frequency aliasing encountered during down-sampling. The operator's efficacy has been assessed across diverse computer vision problems, consistently yielding improved performance ... | Rebuttal 1:
Rebuttal: 1. Thank you for your sincere suggestion. We incorporating a comprehensive efficiency analysis including as following and above Table in the attached file. We will add them to further demonstrate the effectiveness and efficiency of the FouriDown operator in the revised version.
2. Indeed, while th... | Summary: In this research, the authors re-examine the spatial down-sampling mechanism and analyze the biased impacts that arise from the static weighting strategy used in previous methods. To overcome this limitation, this work proposes a new down-sampling paradigm called FouriDown, which incorporates frequency-context... | Rebuttal 1:
Rebuttal: W1. Thank you for your sincere suggestion. We agree with your suggestion and will move the proof of formulas to the supplementary material. This will allow us to incorporate more relevant experiments and analysis into the main text, highlighting the superiority of our framework over other down-sam... | Summary: This paper proposes a downsampling method in Fourier domain (FouriDown) for several low-level vision tasks and an image classification task. FouriDown is supported by theoretical background proven by the authors. While this work improves multiple vision tasks when compared to previous anti-aliasing downsamplin... | Rebuttal 1:
Rebuttal: 1. Recently, many recent studies have been centered on downsampling [reference] as stated in the related works, thus we believe the downsampling is sufficient as a crucial research topic. Our work also has ample contributions for the following reasons:
(1) Our study is the first to revisit the dee... | Summary: - This paper explores a new method for downsampling, by factoring it into shuffling and superposing. Firstly, the authors provide an understanding of the aliasing in deep neural networks from a spectrum view.
- FouriDown, a unified downsampling approach with the learnable and context-adaptive down-sampling... | Rebuttal 1:
Rebuttal: 1. **FouriDown Computation**:
Thank you for your question. The FouriDown incorporates the Fourier transform and its inverse, which traditionally have a computational complexity of \(O(N \log N)\).However, this part has been accelerated by pytorch library and does not occupy any FLOPs. Moreover, th... | Rebuttal 1:
Rebuttal: Thank all reviewers for insightful comment. We recognize the importance of providing comprehensive benchmarks for our proposed method. As shown in table below, we include results from traditional downsampling techniques like bicubic, bilinear, pixel-unshuffle, 2x2 learnable CNN (with stride=2), ma... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a downsampling method for neural networks in the frequency domain.
The proposed downsampling consists of discrete Fourier transform, shuffling, superposing, and inverse discrete Fourier transform.
The shuffling shuffles the patches in Fourier domain and the superposing adds the patches with... | Rebuttal 1:
Rebuttal: Firstly, we appreciate your acknowledgment of the topic and methodological design of our work. Complete and meaningful of the comparisons and analysis has been complemented by visualizations and tables in the rebuttal phase. If this paper is accepted, we commit to meticulously revising and organiz... | null | null | null | null | null | null |
The Equivalence of Dynamic and Strategic Stability under Regularized Learning in Games | Accept (spotlight) | Summary: This paper studies the long run behavior of no-regret learning, and introduces the notion of resilience to strategic deviations as a metric with which to characterize no-regret learning algorithms. Moreover, to further strengthen their results the paper utilizes the idea of setwise strategic stability (m-club)... | Rebuttal 1:
Rebuttal: We are sincerely grateful for your detailed input and positive evaluation. We reply to your questions below and we will revise our manuscript accordingly at the next revision opportunity:
1. "*I feel Section 3 is quite long and the notation is unnecessarily heavy, most of these could be in the ap... | Summary: The paper shows the convergence of regularized learning in games to sets satisfying a property called closeness under better replies. Moreover, convergence rates are derived even with bandit feedback.
Strengths: The pointwise behavior of regularized learning in games has gained lots of attention recently, an... | Rebuttal 1:
Rebuttal: Thank you for your detailed input and positive evaluation. We reply to your questions below and we will revise our manuscript accordingly at the next revision opportunity:
1. "*The paper is [notation-heavy and considers a very general setting] It would be much easier to follow if the authors coul... | Summary: The paper deals with a fundamental question about long-term behavior of regularized learning algorithms in finite player finite action static games. They prove an interesting equivalence between the set of strategies which are closed under better replies with that of stochastically stable and attracting fixed ... | Rebuttal 1:
Rebuttal: Thank you again for your encouraging input and positive evaluation. We reply to your questions below and we will revise our manuscript accordingly at the next revision opportunity:
1. "*Is there any characterization on size of m-club set given certain regularity structure of game. This will prov... | null | null | Rebuttal 1:
Rebuttal: Dear AC, dear reviewers,
We are sincerely grateful for your time, input and positive evaluation. To streamline the discussion phase, we reply to each reviewer's questions in a separate point-by-point thread below.
Thank you again for your time and positive input. Kind regards,
The authors | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Predicting Global Label Relationship Matrix for Graph Neural Networks under Heterophily | Accept (poster) | Summary: The paper proposes a Low-Rank Graph Neural Network (LRGNN) that can model both homophilous and heterophilous graphs via signed propogation (two nodes with the same class label share a positive edge and with different class share a negative edge). The authors predict the label relationship matrix by solving a r... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive acknowledgment on the clarity, novel, experiments, and presentation of our paper. For the weakness part, we have indeed discussed these limitations in the last section of our paper and devote much efforts to support the robustness of LRGNN by presenting exten... | Summary: This paper introduces sign links to model both the homophilous and heterophilous relationships in the global graph, thus extending the applicability of GNNs beyond heterophily. Additionally, the paper leverages the weak balance theory to support the existence of a global low-rank structure in the signed graph ... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions for improving our paper and for appraising the quality and clarity of our paper. Here are our responses to your questions, and we put the tables in the uploaded pdf file.
**Q1 On the differences between LRGNN and existing models.**
**R1** Thanks for the su... | Summary: Graph Neural Networks (GNNs) do not operate effectively when the graph is heterophilic with respect to the task. Addressing this issue is an important problem. The paper starts by analyzing SignedGNN model and demonstrates through a simple analysis that SignedGNNs might be implicitly implementing balance theor... | Rebuttal 1:
Rebuttal: Thank you for your constructive and valuable comments! Here are our responses to your concerns.
**W1 The differences between LRGNN and existing GNNs.**
**R1** We put the discussion comparing LRGNN and GloGNN [1] in Appendix Sec. E, please refer to it for a detailed description. Please refer to ... | Summary: This paper consider predicting the global label relationship matrix as a low rank matrix completion problem. This formulation is based on the observation that the rank of the global label relationship matrix shall equal the number of classes. The authors proposed an efficient solution to the low rank matrix co... | Rebuttal 1:
Rebuttal: Thank you for your positive comments and for taking the time to review our paper. We appreciate your insights and would like to address your concerns.
First, regarding our optimization algorithm for the matrix completion problem, we would like to provide some additional information.
The optimiza... | Rebuttal 1:
Rebuttal: Dear Reviewers and AC,
In response to the concerns raised by Reviewer jP2E, we have presented tables that juxtapose LRGNN with existing baselines across a spectrum of recent and large-scale datasets. This should help alleviate concerns regarding the scope of datasets used in our initial study. To... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces the LRGNN method, which aims to enhance the performance on heterophilous graphs by re-constructing the signed relationships. The approach involves optimizing the low-rank signed matrix using the SVD-free LRMF strategy.
Strengths: The utilization of the low-rank signed relationship matri... | Rebuttal 1:
Rebuttal: Thanks for the detailed review. We address the concerns in below.
**Q1: The reliance on the size of the observation set**
**R** In order to align with the time complexity of other GNNs, we define the observation set as the edge set. However, it is important to note that the performance of the GN... | null | null | null | null | null | null |
Training Transitive and Commutative Multimodal Transformers with LoReTTa | Accept (poster) | Summary: This paper presents a learning paradigm to account for missing paired modalities during training. For example, for three modalities A, B, and C, the proposed system can be trained on (A, B) and (B, C) paired data but transfer to (A, C) or (A, B, C) paired data. To tokenize the modalities, the authors uses stan... | Rebuttal 1:
Rebuttal: **Dear Reviewer SR2A,**
We appreciate your time and effort in reading our manuscript and providing us with valuable feedback. It is wonderful to hear that the soundness, presentation, and contributions of our paper are considered excellent. We would like to address the remaining questions below.
... | Summary: This paper proposes a new method LoReTTa (Linking mOdalities with a tRansitive and commutativE pre-Training sTrAtegy) to address the setting where not all modality pairs are available during training and inference. Concretely, it leverages casual masked modeling and transitive modeling to accomplish the object... | Rebuttal 1:
Rebuttal: **Dear Reviewer FSDR,**
It is a great pleasure to hear that our work addresses a practical and meaningful problem. Our proposed algorithm is indeed simple but has a big impact. We demonstrated this by applying our novel method to a real medical problem. The results we saw were fully consistent wi... | Summary: This paper aims to remedy the problem of training a mobility to perform well on any combination of modalities in the training data, regardless of if these combinations appear in the training data. This is done through commutative and transitive pretraining: the former allows for the model to learn modality A f... | Rebuttal 1:
Rebuttal: **Dear Reviewer 7Goz,**
We sincerely appreciate your critical insights and expertise during the review of our manuscript. We are greatly encouraged by your recognition that our work addresses an important and understudied problem. Such recognition underscores the relevance of our research and fur... | Summary: In this paper, the authors introduce the problem of learning from multi-modal data (e.g. modalities A, B, and C) if not all modality combinations are available at training time (e.g. only paired data (A, B) and (B, C) are given). This constitutes a highly relevant research problem, as learning from multiple mo... | Rebuttal 1:
Rebuttal: **Dear Reviewer QEx4,**
We are very grateful for the thorough review and appreciate that our proposed method is considered novel and highly relevant, showing significant and consistent improvements across different baselines. While we have addressed many of the Reviewer's concerns in the global r... | Rebuttal 1:
Rebuttal: **Dear Area Chair and Reviewers,**
We are very grateful for your insightful comments and sincerely appreciate the time and effort you have taken to provide constructive feedback. It is gratifying to hear that all reviewers unanimously agree that the paper is well-written and well-motivated. They ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a new self-supervised learning method to pretrain from multiple modalities. More specifically, the paper argues that it is hard to obtain datasets with 3 aligned modalities (e.g., text, images, and speech) and tries to address this issue. This can be especially cumbersome for medical dataset... | Rebuttal 1:
Rebuttal: **Dear Reviewer u4o4,**
Thank you for pointing out that our proposed method improves on strong baselines such as BERT and GPT by combining powerful ideas and applying them to real-world problems. In the comments below, we address each of the remaining concerns that were raised.
**Scale and scope... | Summary: This paper proposes a self-supervised learning framework, LoReTTa (Linking mOdalities with a tRansitive and commutativE pre-Training sTrAtegy), for multimodal learning with not-aligned multimodal datasets. To be specific, LoReTTa (i) first trains an autoregressive model for generating A->B and B->C, and then (... | Rebuttal 1:
Rebuttal: **Dear Reviewer a22R,**
We appreciate your valuable comments on our paper. Your recognition of the motivation and broad applicability of our proposed framework is very encouraging. Below, we would like to address the open questions.
**Limited methodological novelty.** In the global rebuttal, we ... | null | null | null | null |
Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses | Accept (poster) | Summary: The paper presents multiple extensions of Haim et al. [2022] in relation to reconstruction of training data in neural networks. In short the extensions are: extension to multiclass from binary, extension to regression losses, investigate of the effect of weight decay and analysis of relationship between number... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review and constructive and positive feedback.
Regrading general vs. regression loss: we agree with the reviewer on the comment. Although our method described in Section 5 allows us to reconstruct data which is trained using any loss function, we tested it o... | Summary: Reconstructing training samples from the trained model may cause privacy issues. This paper demonstrates the training samples reconstruction for multi-classes and also for both fully-connected neural networks and convolutional neural networks. In addition, the authors investigate different factors that contrib... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and feedback. Regarding the questions raised by the reviewer:
1) Our work considers relatively small-scale networks, trained on small datasets (up to $5$,$000$ samples). Although the generalization of our models is not comparable to the state of art, it is ... | Summary: This paper is following the work of Haim et al. (2022) who introduced a reconstruction scheme for neural network with logistic or exponential loss for binary classification tasks. They extended this work by introducing this reconstruction scheme in a multi class setting. They also demonstrated how it can also ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and constructive feedback.
Regarding the paper's storyline: this is a good point, and we had a dilemma on how to organize the different sections. We will consider implementing the reviewer's suggestion for reordering in the camera-ready version.
Regarding m... | Summary: This work extends the work of [Haim et al. 2022] to multi-class classification and with a more general loss function. Through mainly empirical evaluations, the authors demonstrate that data reconstruction is also possible and reveal interesting relationships between the ability to reconstruct and the number of... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review and the constructive comments.
1) Regarding novelty, we would like to emphasize that although our work is a direct extension of [Haim et al. 2022], we make some significant and novel contributions and overcome some of the limitations of the previous w... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization | Accept (poster) | Summary: This paper proposes EAGLE to improve OOD generalization in DGNNs. It focuses on modeling and inferring complex environments on dynamic graphs with distribution shifts and identifying invariant patterns within inferred spatio-temporal environments. The EAGLE incorporates an environment-aware model, an environme... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the detailed comments. Responses are as follows.
**Q1.1:** The definition of “environment”.
**A1.1:** Thanks for your comment. We would clarify that we have explained “environment” from the perspective of **latent factor** (lines 88-94), **real-world explanati... | Summary: This paper proposes a novel framework called EAGLE to address the challenge of out-of-distribution generalization in dynamic graphs. The authors investigate the impact of latent environments on dynamic graphs and develop methods to model and infer these environments. They propose techniques to recognize spatio... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the detailed comments and insightful questions. We make responses to the reviewer’s comments as follows.
**Q1:** Variable symbols are kind of complicated, and their expressions are not very clear, which brings difficulties in understanding theoretical proofs.
... | Summary: In this paper, the authors propose a novel framework EAGLE, Environment-Aware dynamic Graph LEarning, which tackles the OOD generalization problem by modeling complex dynamic environments and exploiting spatial-temporal invariant patterns. Following the Environment “Modeling-Inferring-Discriminating-Generalizi... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the detailed comments. Responses are as follows.
**Q1.1:** Whether EAGLE requires the environment label?
**A1.1:** Thanks for your question. EAGLE doesn’t require the ground-truth environment labels, which are also **inaccessible** in the real dynamic graphs. ... | Summary: This paper proposes a novel framework called EAGLE for out-of-distribution generalization on dynamic graphs. EAGLE models the complex environments that influence the generation of dynamic graphs and exploits the spatio-temporal invariant patterns that can generalize under distribution shifts. EAGLE consists of... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the detailed comments and insightful questions. Responses are as follows.
**Q1:** The generated environment patterns lack explainability.
**A1:** Thank you for your question.
- **[Understand latent environments]** Environments are **latent factors**, where t... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Clifford Group Equivariant Neural Networks | Accept (oral) | Summary: This paper presents a novel approach for creating E(n)-equivariant models based on Clifford (geometric) algebras.
The authors identify the Clifford group and its action. By extending this action to the entire Clifford algebra, remarkable properties can be obtained enabling the construction of equivariant map... | Rebuttal 1:
Rebuttal: We thank the reviewer for their highly valuable feedback. We address their concerns and questions in the following. The reviewer has already transformed weaknesses into questions (thanks!), which we answer directly.
1. *How is translation equivariance obtained?*
a. Translation invariance is ... | Summary: I like to admit that I am not familiar with this area of work. I try my best to review the key contributions of this work. My review is more of clarification than critic.
The theory part of the paper lies mainly in section 3.1 starting with Eq.(5).
The computation part of the paper is given in section 4. The ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the actionable feedback. We address their concerns and questions in the following.
1. *Familiarize readers with the Clifford algebra, perhaps with a tutorial.*
a. Following up with a tutorial is a great suggestion! Indeed, we are already working on a blog post serie... | Summary: In this submission the authors introduce an equivariant neural network architecture building on the Clifford algebra (defined by a vector space and a quadratic form).
They remind that this algebra admits an orthogonal decomposition over sub-vector spaces called grade $m$ where $m$ is the number of basis elemen... | Rebuttal 1:
Rebuttal: We thank the reviewer for the actionable feedback. We address their concerns and questions in the following.
1. *Data embedding?*
a. The data is embedded by an additional multivector dim in the network tensors (response to reviewer W8CU), whose size depends on the algebra, e.g., 4 or 8 for 2... | Summary: The paper introduces a new method for constructing E(n)-equivariant networks called Clifford Group Equivariant Neural Networks (CGENNs), based on an adjusted definition of the Clifford group, a subgroup of the Clifford algebra. The researchers have shown that the group’s action forms an orthogonal automorphism... | Rebuttal 1:
Rebuttal: We thank the reviewer for the actionable feedback. We address their concerns and questions in the following.
1. *Accessibility and introductions of notations.*
a. During our literature research, we quickly found out that there are at least three communities (physicists, mathematicians, compu... | Rebuttal 1:
Rebuttal: We extend our gratitude to the reviewers for their efforts in evaluating our paper. The overall positive response we received has been highly encouraging. The feedback has undoubtedly improved the quality of our work, and we sincerely appreciate the insightful comments and suggestions that were pr... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper develops a new kind of neural network model based on the Clifford group, which makes the neural network E(n)-equivariant. The model is tested on a number of datasets, showing that it is capable of recovering the desired structure from the data.
Strengths: This seems like an interesting and importa... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging feedback. We address their concerns and questions in the following.
1. *What is being computed in, e.g., equation 11?*
a. As input data, we consider points in a vector space $V$, for example, $\mathbb{R}^3$. These can be locations, velocities, forces... | null | null | null | null | null | null |
Learning with Explanation Constraints | Accept (poster) | Summary: This paper tries to answer an important and fundamental question: why can interpretability constraints on model parameters improve model performance. The authors developed an analytic framework. They first define constraints as a functional on a model function and a data point, and the model satisfying the con... | Rebuttal 1:
Rebuttal: > The paper lacks a discussion on the stringency of their conditions in theorems, which makes it more difficult to judge the significance of the contribution. The paper focus on gradient constraints and lacks a discussion and related work on model shape constraints, such as monotonicity and convex... | Summary: Traditionally, learning from labelled examples has been well studied. However, with the advent of large models (and few-shot learning) and risk of misspecifications (spurious correlations), need for augmenting supervision such as with example explanations is gaining wider interest.
This paper deals with this t... | Rebuttal 1:
Rebuttal: > 1. How are explanations different? Fundamentally, I did not understand why explanation constraints are given a status different from example labels. Can we not simply consider them to be additional labels and minimize the joint risk such as in the Lagrangian method or Rieger et.al. and handle th... | Summary: In this work, the authors theoretically investigate learning with constraints on the gradient of the model. Like other works in a similar vein, this paper focuses on gradient-based explanations and on neural networks. Unlike related works, however, this paper formulates explanation constraints as being over t... | Rebuttal 1:
Rebuttal: > 1. The main drawback of the paper is that the theory and experiments are almost wholly disjointed. The first portion of the paper focuses specifically on theoretical guarantees using standard learning theoretic tools; however, I do note see where any of these tools are experimentally evaluated e... | Summary: Disclaimer: I am not an expert in Learning Theory, but I come from a background of interpretability and explanation model’s decision making. So I am familiar with the methods that this work is trying to describe and some related works. I will review this paper modestly, and hope authors and AC can understand. ... | Rebuttal 1:
Rebuttal: > I do not observe any major weaknesses in this work. Although, in terms of writing, the transition into section 4.1 was a bit of a jump. It was not immediately clear to me why there is a need for variational method. Although in experiments it shows to obtain better performance that non-variationa... | Rebuttal 1:
Rebuttal: **General response**
We would like to thank all the reviewers for their efforts in providing thoughtful and attentive examinations of our work. We are glad to see that the reviewers highlighted a number of strengths, including:
1. The paper tries to answer an “interesting”(o4F1) and “important” ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents the concept of learning from explanations and theoretically shows that learning from explanations can improve the standard excess risk bounds in the agnostic setting. They treat explanations as constraints in the ERM procedure and introduce an extension of PAC model referred to as EPAC mode... | Rebuttal 1:
Rebuttal: > 1. Overall I like the ideas presented in the paper but it was a bit tedious to get exactly what is being done. It first appeared that the paper is proposing a method to learn the explanation functionals i.e. learning a model that can generate explanations by learning from some labeled explanatio... | null | null | null | null | null | null |
Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity | Accept (spotlight) | Summary: The paper studies distributed learning in the presense of Byzantine workers (that is, nodes that output adversarial results) and data heterogeneity.
A previous notion of robustness for distributed learning, namely G-gradient dissimilarity, is not sufficient to capture data heterogeneity. In fact, this holds e... | Rebuttal 1:
Rebuttal: Thank you for the encouraging comments. Please find below answers to your questions.
>What is the (G, B)-gradient dissimilarity in the experiments?
Intuitively, $(G, B)$-gradient dissimilarity measures data heterogeneity using the gradient of the empirical loss function. For a given distributed ... | Summary: This paper considers under the robust distributed learning settings, the conventional learning error bounds are vacuous as they rely on restrictive assumptions as $G$-gradient dissimilarity (equation 2). The paper relaxes the assumption to a more general $(G, B)$-dissimilarity model that relaxes the bounds on ... | Rebuttal 1:
Rebuttal: Thank you for the encouraging comments. Please find below answers to your questions. We hope that you would increase the score if you find our answers satisfactory.
>I am confused on the heuristic method to compute and in Appendix D.3, line 743. Could you explain such formula?
First, we recall t... | Summary: The work focuses on the analysis of the limits of Byzantine-robust learning under so-called $(G,B)$-dissimilarity assumption -- a generalization of the standard bounded gradient dissimilarity assumption. The main result of the paper is in establishing the upper bound for the maximal admissible ratio of Byzanti... | Rebuttal 1:
Rebuttal: Thank you for the encouraging comments. We will implement your suggestions in the final version of the paper. Please find below answers to your questions.
>The results of Theorem 2 are not completely novel. In particular, there exist results under $(G,B)$-dissimilarity assumption, see Theorem V f... | Summary: In the present paper the authors study the problem of distributed learning in the presence of adversarial learners (aka Byzantine workers) and hetogeneous data. The paper aims to generalize and improve previous results on homogeneous data, an assumption that is claimed to be very restrictive in practice. For t... | Rebuttal 1:
Rebuttal: Thank you for the encouraging comments. We will implement your suggestions in the final version of the paper. Please find below answers to your questions.
> p.2: Maybe you can make the connection between the G and (G,B) assumptions and bounded / affine variance assumptions in the analysis of SGD.... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
DRAUC: An Instance-wise Distributionally Robust AUC Optimization Framework | Accept (poster) | Summary: This paper considers AUC optimization in the distribution shift setting, which is of great value to the machine learning community. The author formulates the problem as a min-max-min-max problem and relaxes it as an upper bound minimization problem. The experimental results also confirm the effectiveness of th... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments! Our response is as follows:
>**Q1:** Table 1 shows the results of CIFAR10-LT, but there is no description of the experimental settings in the context. The proposed method performs poorly on CIFAR10-LT, which seems to contradict the claim of the article. How c... | Summary: Area under the ROC curve (AUC) has been widely used for imbalanced classification, but optimizing the metric requires the assumption that training and test data come from the same distribution, which is not realistic. The authors tackle to explore distributionally robust optimization under the context of AUC o... | Rebuttal 1:
Rebuttal: Thank you for your constructive reviews! To address your concern, we conduct several more experiments on larger datasets, and expand our method toward multi-class settings.
>**Q1:** Experiments are only conducted on small-scale datasets. It may need to validate on more realistic large-scale data... | Summary: The paper proposes an end-to-end framework to optimize the distributional robust AUC.
The authors address several key challenges of this task and robust generalization can be achieved with theoretical guarantee.
Experiments on several corrupted long-tailed benchmarks demonstrate the superiority of the proposed... | Rebuttal 1:
Rebuttal: Thank you for your constructive concerns! We would like to clarify the following questions:
>**Q1:** The proposed method is only evaluated on small-scale datasets such as MNIST and CIFAR-10/100. It remains unclear whether the proposed method can be scaled to large datasets.
**R1:** Thank you for... | Summary: The authors propose a Distributionally Robust AUC (DRAUC) optimization method that combines Wasserstein distance based DRO with AUC optimization. The combination is designed to be robust for data perturbations. The authors provide theoretical analysis from perturbation and generalization perspective, and justi... | Rebuttal 1:
Rebuttal: Thank you for your valuable questions! To address your concern, we restate our contributions, fix the typos, and add AUCDRO as a new baseline. The details are as follows:
>**Q1:** The idea is straightforward and the contribution is limited. Perturbation robustness idea has been explored for adver... | Rebuttal 1:
Rebuttal: Dear reviewers,
we sincerely appreciate your time and valuable comments. Thanks to your detailed, insightful reviews, we introduce the following changes:
- **Additional Empirical Support:** We add AUCDRO[a] as a new baseline. We conduct experiments on **large-scale datasets** including Melanoma a... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Fast Optimal Transport through Sliced Generalized Wasserstein Geodesics | Accept (spotlight) | Summary: The paper introduces a variant of slicing of the Wasserstein distance that keeps the optimal transport map property (in particular, existence of an optimal transport map). The typical formulation of slicing loses the Monge map as each slice comes with its own map and there is no natural way to combine maps com... | Rebuttal 1:
Rebuttal: We thank you for your review and for the positive evaluation of our paper. We now provide detailed answers to your questions.
- **Existence of minimizer in Definition 4.1.** The existence of $\mu_\theta^{1\to2}$ is always guaranteed, whenever $\mu_1,\mu_2$ are two empirical probability measures. ... | Summary: This paper proposes an approximation to the squared Wasserstein distance between probability measures (typically required in high dimensional space) based on the sliced Wasserstein paradigm. The general idea is to project points onto a line where computing Wasserstein distances becomes significantly simpler (a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and provide some answers to the questions and provide feeback about the reported weaknesses.
- **The practical aspect of min-SWGG is underwhelming.** In the paper, we conducted several experiments in a wide set of scenarii, with the aim to stress th... | Summary: This paper proposed a novel proxy of the squared Wasserstein distance called min-SWGG, which is based on Sliced-Wasserstein distance (SWD) and the projected Wasserstein distance (PWD). This quantity is also shown to be relevant to Wasserstein Generalized Geodesics with pivot measure, which makes it more intere... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comment regarding the behavior of min-SWGG when $d$ varies. It is indeed an interesting question, and we do believe that min-SWGG is a particularly interesting approximation in this context. First, we recall that, when $d > 2n$, equality between $W_2^2$ and min-SWGG h... | Summary: The paper proposes a new formulation and algorithm to approximate Wasserstein 2-distance (i.e., the distance of two points is the squared Euclidean one) between two distributions. Though some of the development is general, the key parts and the algorithms apply only to distributions being the sums of delta fun... | Rebuttal 1:
Rebuttal: We thank you for your work in revising our paper and for the valuable comments and questions.
- **The formulation of min-SWGG is straightforward**. We respectfully disagree. You are correct in stating that PWD and min-SWGG share some similarities. While the definition of min-SWGG might appear sim... | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive comments and answer their questions below. We hope our clarifications address the reviewers' questions.
Reviewers 5WTB and CJnA discussed the fact that the min-SWGG formulation is derived in the restricted $\mathcal{P}_2^n(\mathbb{R^d})$ setting and ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
2Direction: Theoretically Faster Distributed Training with Bidirectional Communication Compression | Accept (poster) | Summary: This paper proposes 2Direction, a new GD-based distributed training algorithm for convex optimization problems. The improvement of 2Direction over previous works that target communication complexity in the convex case is that 2Direction is both accelerated and allows for bi-directional compression. 2Direction ... | Rebuttal 1:
Rebuttal: Thank you for the review!
> The communication complexity analysis and its optimization are not fully clear. Specifically, there is dependency between the round cost and the number of communication rounds (i.e., via K and w).
* The rounds costs and the number of communication rounds depend on $K$... | Summary: This paper proposes a new distributed convex optimization problems with bidirectional compression. This algorithm, named as 2Direction, is claimed to be the first that improves upon the communication complexity of the vanilla accelerated gradient descent.
Strengths: 1. The 2Direction method seems novel
2. The... | Rebuttal 1:
Rebuttal: Thank you for the comments!
> The proof of the algorithm is rather complicated. I cannot verify the solidness of the established theorem given the very tight review timeline. The appendix I, J, K, L and O are very scary given the symbolically computed expressions. It is basically a computer-aided... | Summary: This paper studies 2Direction, a new method for compressed communications for the centralized distributed convex optimization problem, in the case where both worker-to-server and server-to-worker communications are compressed. Compared to previous work EF21-P+DIANA, it guarantees a total communication complex... | Rebuttal 1:
Rebuttal: Thank you for the positive evaluation and comments!
> While efforts were made to ease the understanding of the general ideas behind the algorithm and proof, they seem so cluttered that it deters from delving into the technical details.
Unfortunately, this is the nature of all accelerated methods... | Summary: The authors considered a distributed convex optimization setting where both uplink and downlink communications should be considered. In this setting, the authors proposed an accelerated method called 2Direction which generalizes AGD into the bidirectional compressed communication setting and prove its converge... | Rebuttal 1:
Rebuttal: Thank you for the review!
> For this **new setting**, the authors propose new strategies to make the adaption of AGD possible, which is nontrivial. Meanwhile, the bounds seem OK to me and can be applicable in the smooth transition from uplink to downlink.
> Such a generalization for the bidirect... | Rebuttal 1:
Rebuttal: Dear AC and Reviewers,
Thank you for your work and effort. In this comment, we quickly clarify our main contribution to the community.
The field of optimization methods with compressed communication methods is very popular, rapidly growing, and will only increase in importance (e.g., see Jue Wan... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian, and Beyond $1+\alpha$ Moments | Accept (poster) | Summary: The paper studies one-dimensional mean estimation in a beyond-worst case setting.
The main contributions are twofold. First, it gives indistinguishability results which, given a distribution p of finite mean, construct a “partner” distribution q that preserves the moments yet has mean far from p. This implies... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment of our work and for your questions. We address your comments below.
-----
**High dimensional setting**: This is an interesting question, and would likely need the full extent of the notion of neighborhood optimality to address, going beyond the "$p$ versus $... | Summary: This paper is about analyzing the optimal bounds for mean estimation, beyond the worst-case setting given in the recent breakthrough result of Lee and Valiant giving optimal subgaussian error rates for all distributions with unknown, finite variance. This prior work shows only that there exists a single "probl... | Rebuttal 1:
Rebuttal: Thank you for your positive and in-depth review of our paper.
Regarding your comment about the neighborhood optimality definition, we want to emphasize that, prior to this paper, the community would basically just use the local minimax definition. As we show in Appendix A, local minimax allows me... | Summary: This paper studies the fundamental task of 1-dimensional mean estimation. There has been recent interest in the community to understand “beyond worst-case” analyses of statistical problems, where guarantees are able to encompass properties of instances that may make them more tractable than worst-case instance... | Rebuttal 1:
Rebuttal: Thank you for your insight on our work, and your questions. We particularly enjoyed reading your summary of the strengths of the paper.
Here we respond to the questions raised in the review.
**Q1**: What we meant is that, in order to beat the sub-Gaussian bound for *any* distribution, even asymp... | Summary: This paper studies the 1-dimensional robust mean estimation from the perspective of "beyond the worst-case analysis". They show somehow a negative result, that is, for any distribution, we can always construct another distribution that is close in probability distance, but their means are well-separated. Furth... | Rebuttal 1:
Rebuttal: Yes, one could use the framework of this paper to attempt to design a construction for symmetric distributions $p$ (constructing a symmetric alternative, $q$), and thereby obtain a Fisher information rate estimation lower bound. In fact, (as discussed in [GLP23]) the Fisher information rate bound ... | Rebuttal 1:
Rebuttal: We thank the reviewers for the positive assessment of our work, and of course also for the constructive reviews and many interesting questions raised.
We are particularly encouraged that the reviewers find our paper 1) well-motivated/important (Reviewers iso5, bzea), 2) an extremely fundamental pu... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Thinker: Learning to Plan and Act | Accept (poster) | Summary: This paper presents *Thinker*, a method for augmenting an MDP with a learned model so that model-free algorithms can perform “planning” on the augmented MDP. Specifically, Thinker creates a cross-product MDP between the “real world” and a learned world model. This augmented MDP has a union action set that enab... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments and detailed feedback! We appreciate the reviewer’s concise summary of the proposed algorithm and acknowledgement of our experiments. The weaknesses are addressed as follows:
**(R1) Algorithm’s benefits seem to plateau after K=10-20 planning ste... | Summary: The authors propose Thinker, a model-based RL algorithm that transforms a MDP into one where at each step, the agent is required to generate plans in imagination before selecting a real world action. Then through the RL gradient, the agent learns to plan in order to act. The authors get good results in Atari a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments and detailed feedback! We appreciate the reviewer recognizing the potential of our paper while also raising valid concerns regarding the experiments. The weaknesses are addressed as follows:
**Insufficient baselines** - Please refer to (G1) in t... | Summary: - This paper introduces a novel algorithm, Thinker, which learns how to plan by interacting with a learned world model using “imaginary actions”—negating the need for hand-designing a planner. The algorithm produces SOTA results on Sokoban, and its efficacy on Atari 2600 is demonstrated as well.
Strengths: -... | Rebuttal 1:
Rebuttal: We thank the reviewers for the constructive comments and detailed feedback! We appreciate the reviewer’s recognition of our work. The weaknesses are addressed as follows:
**Insufficient baselines** - Please refer to (G1) in the global response.
**Clarity of Figure 1** - We will use a bordered w... | Summary: This paper presents a model-based RL approach, Thinker, that learns a world model and plans to take actions by generating imaginary rollouts with the learned world model. The paper claims that Thinker is a general method for any RL algorithms and does not rely on any hand-crafted planning algorithm. Experiment... | Rebuttal 1:
Rebuttal: We thank the reviewer for the questions. The questions raised are addressed as follows:
**Insufficient baselines** - Please refer to (G1) in the global response.
**The difference with MuZero** - Please refer to the first three paragraphs of the global response and reviewer #fWg3’s summary of th... | Rebuttal 1:
Rebuttal: We thank the reviewers for the constructive comments and detailed feedback. We appreciate that most reviewers recognize the novelty and value of our proposed algorithm, Thinker. We note that some reviewers seem to underestimate the novelty, viewing Thinker as merely a variant of MuZero, or focusin... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work presents Thinker, a new model-based reinforcement learning algorithm that achieves state-of-the-art performance on the puzzle video game, Skoban. The main contribution made by the authors is using a dual network architecture which addresses the sample inefficiency problem that other algorithms such a... | Rebuttal 1:
Rebuttal: We thank the reviewers for the constructive comments and detailed feedback! The summary gives a clear description of the proposed dual network, but we would like to point out that the main focus of the paper is the augmented MDP and learning to plan (thus the paper’s title), and dual network is on... | Summary: This paper presents the Thinker algorithm, a method that augments an MDP such that an agent takes 'imaginary actions' in a learned model prior to executing actions in the environment. This allows the agent to incorporate planning strategies into a policy learned with RL. State-of-the-art performance is achieve... | Rebuttal 1:
Rebuttal: We thank the reviewers for the constructive comments and detailed feedback! The weaknesses are addressed as follows:
**Insufficient baselines** - Please refer to (G1) in the global response.
**Multiple moving parts** - Please refer to (G2) and (G3) in the global response for more ablation analys... | null | null | null | null |
Reliable Off-Policy Learning for Dosage Combinations | Accept (poster) | Summary: The paper proposed a novel method for off-policy learning for continuous dosage combinations that aims to be robust to action space sparsity. To account for drug-drug interactions, they proposed a dosage combination network (DCNet) which leverages a tensor product basis to estimate smooth interaction effects i... | Rebuttal 1:
Rebuttal: Thank you for your review and for giving us the opportunity to clarify certain aspects of our paper.
### Response to “Weaknesses”
1. Thank you for raising this important point. Our framework can be easily extended to much higher dimensions. To demonstrate this, we added new results for higher dim... | Summary: (I have more background in offline RL than causal inference, and my description below may include some RL terminologies as the two are closely related.)
This submission studies the problem of off-policy learning of dosage recommendations in personalized medical decision making, using a causal inference frame... | Rebuttal 1:
Rebuttal: ## Response to “Weaknesses”
- **Importance of modeling _continuous_ treatments.** Many applications in medicine rely on continuous treatments where discretization would often miss the optimal solution for patients (e.g., chemotherapy).\
We performed additional experiments to demonstrate the gain o... | Summary: The paper deals with an important and interesting problem in personalized decision making. In particular, the paper considers the setting when there are multiple continuous treatments available and models the joint effect of dosage combinations. A novel off-policy learning algorithm is proposed to solve the pr... | Rebuttal 1:
Rebuttal: Thank you for your detailed, constructive, and positive feedback!
### Response to “Weaknesses”
1. **Importance of continuous vs. discretized treatments.** Thank you for introducing this very interesting and related additional literature to us!\
We understand the importance of benchmarking our fra... | Summary: The paper introduces a new methodology to derive a policy to assign individualized dosage combinations relying on retrospectively collected observational data. The method relies on off-policy learning and try to account for the joint effect of multiple dependent dosages. A neural network is used to estimate in... | Rebuttal 1:
Rebuttal: Answer:
Thank you for your detailed and constructive feedback. We improved our paper in the following ways:
## Responses to “Weaknesses”
- Thank you for giving us the opportunity to clarify the optimization part in our paper. We agree that our final objective in Eq. (7) is non-concave in the neur... | Rebuttal 1:
Rebuttal: Thank you very much for the constructive and positive evaluation of our paper and your helpful comments! We addressed all of them in the comments below and uploaded additional results as a PDF file.
Our main improvements are the following:
- We provide further **extensive experiments**: \
(1) We... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
DP-SGD Without Clipping: The Lipschitz Neural Network Way | Reject | Summary: - The paper investigates the use of Lipschitz constrained networks to replace clipping functions and limit gradient sensitivity in DP-SGD.
- Lipschitz constrained networks are utilized as an alternative to clipping in order to address the issues of clipping's impact on convergence and performance in DP-SGD.
... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and your challenging questions.
#### **Question (i)**
Note that the scalar-scalar form is not an approximation: it is a valid upper bound (the “=” symbol is a typo, it is actually “<=”) coming from applying Cauchy-Swartz inequality to a matrix-vector product. T... | Summary: Differentially Private (DP) Deep Neural Networks (DNNs) face challenges in estimating tight bounds on the sensitivity of the network’s layers. Instead, they rely on a per-sample gradient clipping process (as argued by the authors). This process not only biases the direction of the gradients but also proves cos... | Rebuttal 1:
Rebuttal: Thank you for your efforts in engaging in this discussion. From what we understand, there are a few points which we need to clarify further.
As for the summary, our main contribution is to make the DP training process quicker and more memory efficient by **eliminating** the need for per-sample gr... | Summary: The paper addresses the problem of efficiently bounding the sensitivity of gradients in DP-SGD by using special architectures the layers of which can be proven to be Lipschitz with respect to the parameters, hence bounded gradient. They show how to recursively calculate the sensitivity of a sequence of layers,... | Rebuttal 1:
Rebuttal: Despite your rating, we note that you recognize the originality of our work and its potential impact, and that you did not find any major issue in our method nor our methodology.
Your concern seems to be about the short term impact of the paper. We are not convinced that beating SOTA results is n... | Summary: The paper studies the question of how to do differentially private optimization without using per-sample gradient clipping, in order to simplify and speedup the iteration cost.
The paper proposes to restrict the class of functions to feed-forward neural networks for which it is feasible to compute bound on the... | Rebuttal 1:
Rebuttal: Thank you for your detailed questions and careful reading.
#### **Remark 1**
We agree with your observation that a direct comparison to vanilla DP-SGD is valuable. Please see our general response for preliminary results on Cifar-10 and on two tabular datasets. Note that there isn’t exactly a 1-t... | Rebuttal 1:
Rebuttal: We are happy to see that the reviewers appreciated the originality and potential of our contributions. According to the reviewers, “the concept is **original and potentially significant**” (reviewer mBgX), “the paper is **interesting** and **novel** and **provides direction for new research**” (re... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models | Accept (poster) | Summary: This article investigates the approximation of the Fokker-Planck equation and the fractional Fokker-Planck equation using positive semi-definite (PSD) models. The authors demonstrate that for a given accuracy $\epsilon$, there exists a PSD model with a dimension not exceeding $\epsilon^{-(d+1)/(\beta - 2s)}(\l... | Rebuttal 1:
Rebuttal: We thank the reviewer for positively assessing our paper. Here below we address the concerns raised by the reviewer.
**Approximation Error with Initial Condition**:
We bound the $L_2$ error of the difference between the true value and approximated value of the (fractional) Fokker-Planck equatio... | Summary: The authors introduced a new method of sampling solutions of the Fokker--Planck equation (FPE) using a positive semidefinite (PSD) model. While the algorithm used for the PSD model is not new, the authors created a framework to sample from the FPE, which is quite original based on my knowledge.
While I belie... | Rebuttal 1:
Rebuttal: We thank the reviewer for assessing our work positively. Here below we address the major concerns raised by the reviewer.
**Sampling Algorithm**: We will add the details of the sampling algorithm in the revision. Here below, we are describing the methodology of [MFBR22] for sampling algorithm for... | Summary: This paper considers numerical methods for the Fokker-Plank equation and its "fractional Laplacian" version. These partial differential equations describe the evolution of densities of certain random processes: in the case of standard FP, the process is a regular diffusion (an SDE driven by Brownian motion), w... | Rebuttal 1:
Rebuttal: Thanks a lot for assessing our work positively. Here below we address the major concerns raised by the reviewer.
**Typos and Presentation**: Thank you very much for pointing us to typos. We will fix the typos and improve the presentation. We will also include suggested citations in our paper. ... | Summary: This paper studies the problem of efficiently sampling from a SDE given a drift function and diffusion tensor. It proposes a solution based on computing a PSD model that satisfies the Fokker-Planck solution associated with the SDE, and then sampling from the resulting PSD model.
Strengths: The paper seems to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for spending time reviewing our work. We agree that our paper is on the technical side; however, we suspect that the reviewer might have found the paper difficult to read if the subject is not close enough to their field of expertise. In such a case, we believe that the stron... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Transient Neural Radiance Fields for Lidar View Synthesis and 3D Reconstruction | Accept (spotlight) | Summary: The paper brings NeRFs to a new dimension of imaging at transient timescales enabling the rendering of transient imagery. It incorporates the underlying image formation model of the lidar and recovers improved geometry and conventional appearance when training on a few input viewpoints. The paper also proposes... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback. Please see the above response to all reviewers where we address questions related to the ablation study of the HDR-informed loss and the space carving regularization. We discuss effects of natural lighting below.
**kaKw: Scanning under natural light... | Summary: The paper describes a variant of NeRF specially designed for the image formation model of a time-of-flight imaging sensor. In contrast to earlier works using ToF sensors in a neural radiance field setting, the method is not designed to merely take depth maps or point clouds from the ToF sensors as input. Inste... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback. Please see the above response to all reviewers where we discuss training on more viewpoints. We also discuss noise modeling below.
**vJ74: Forward model choices–noise modeling**. As we note in the paper, the SPADs used in our hardware prototype foll... | Summary: NeRF is a method that has recently become popular for view synthesis. It allows estimation of a 3D scene density and 5D radiance map using a few intensity images and their camera poses. These can then be used to render intensity and depth images from any novel 2D view.
This paper extends NeRF to allow estimat... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback. Please see the above response to all reviewers where we address questions related to the spatial footprint of the laser/sensor spot. We address other questions related to novelty and applications below.
**Zi1e: Novelty.** Our method is the first to ... | Summary: This work proposes a novel-view synthesis method for active sensors (single-photon LiDAR sensors) based on the Neural Radiance Field formulation. To this end, the volume rendering formulation for active sensors is derived, properly taking into account the measurement formation process (two-way distance, intens... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback. Please see the above response to all reviewers where we address questions related to the experimental evaluation, effects of incidence angle, consideration of the low-view setting, and the depth evaluation. We address other questions below.
**tsWB: ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their helpful suggestions and insightful questions. Our work brings NeRFs to a new dimension of imaging at transient timescales, enabling rendering of photon count histograms from novel views for the first time. Further, we show that supervision on photon count histogra... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Method | Accept (oral) | Summary: The paper presents a thorough consideration on the parametrization of optimizable solution generators that allow using gradient descent to find solutions for combinatorial optimization methods. They find a set of assumptions that enable complete, compressed and efficiently optimizable representations, in parti... | Rebuttal 1:
Rebuttal:
We are very encouraged from the very positive feedback of the reviewer and their excitement about our work and potential impact. We are also very excited about our work and believe that our work will initiate a principled theoretical study of neural combinatorial optimization and its theoretical... | Summary: The paper provides a theoretical analysis of policy-gradient methods for combinatorial optimization problems. It defines a set of three desirable properties one might wish to be fulfilled for such a property, namely being able to generate an approximately optimal solution, being small in size, and enabling eff... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer (i) for finding the contribution of our paper great and for providing extensive constructive feedback, (ii) for pointing out various typos in the current draft, which we will fix in our first revision and (iii) for proposing additional references and suggestions... | Summary: This paper proposes a theoretical framework for analyzing the effectiveness of deep models trained by gradient-based methods as solution generators for combinatorial problems. The authors first investigate the complete, compressed and efficiently optimizable properties of the solution generator on many combina... | Rebuttal 1:
Rebuttal:
We thank the reviewer for the very positive feedback and the provided questions.
> *The authors may want to conduct experiments on more combinatorial problems with larger scales to demonstrate the effectiveness of the proposed method.*
*Response:* We kindly refer to our general response about e... | Summary: This paper deals with a significant question at the intersection of combinatorial optimization and continuous-based optimization: is it possible to design solution generators for combinatorial problems that are (1) expressive enough to generate approximately optimal solutions, (2) tractable so that their param... | Rebuttal 1:
Rebuttal:
We would like to thank the reviewer for the positive feedback and insightful questions.
> - *I understand that the main focus of this work is to provide a generic framework with reasonable assumptions and strong performance guarantees. I feel that the authors have delivered on that premise. Tha... | Rebuttal 1:
Rebuttal: **General Response**
We thank all reviewers for taking the time to read our manuscript carefully and for providing constructive and insightful feedback. We are very encouraged by the positive comments of the reviewers on the novelty and significance of our theoretical framework for Neural Combina... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Causal Framework for Decomposing Spurious Variations | Accept (poster) | Summary: The paper proposes a novel theoretical framework for decomposing spurious causal effects. For this purpose, the authors introduce the concept of partial abduction and prediction. Here, only a subset of the latent variables in a given structural causal model (SCM) is updated when conditioning on observed eviden... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort in sharing his/her thoughts and comments. We respond point-by-point to each of the concerns raised.
---
[W1: Required causal assumptions] Indeed, you are correct that a fully specified graph is needed for Theorem 3. We also agree that this may be c... | Summary: This paper considers the problem of decomposing spurious variations in both Markovian and Semi-Markovian models, which are potentially useful to many real applications. A non-parametric decomposition of spurious effects is given given and identification issue is also discussed under suitable conditions.
Stre... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort in providing his/her feedback. We were encouraged by the fact that the reviewer appreciated the main strengths of the manuscript. We respond point-by-point to the raised concerns.
[W1: Limitations of the developed theory] Thanks for noting this; Plea... | Summary: The authors tackle the problem of decomposing the source of variation in causal analysis. In many real-world problems, there are many factors that can introduce spurious correlation between a treatment and outcome, and breaking down the contribution of each variable to that spurious correlation can be useful ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort in sharing his/her thoughts and comments. We were quite glad an encouraged by the fact that the reviewer appreciated the main strengths of our work. We respond point-by-point and hope to have addressed the raised concerns.
---
[W1: Clearer notation] ... | Summary: This paper provides a new framework for decomposing spurious effects and its identification conditions. In addition, they proposed a nonparametric method for decomposing the spurious effect under sufficient conditions.
Strengths: - In general, this paper is well-motivated.
- It provides a procedure to decomp... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort in providing your feedback. We respond to each point in the sequel and would be happy to provide further clarification if you feel suitable.
---
[W1: Spurious Effect vs. Variation] Thanks; we acknowledge that in the paper the distinction between the... | Rebuttal 1:
Rebuttal: The authors would like to sincerely thank all the reviewers for this paper. The main strengths and novelty were clearly appreciated, and the questions raised were quite useful for us to revise and improve the paper.
In our response, we index all weaknesses with W, questions Q, and limitations L. ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The manuscript studies the decomposition of spurious variations. Specifically, a tool is developed for the non-parametric decompositions in both Markovian and Semi-Markovian models.
Strengths: 1. The considered problem is practical. As stated in the introduction, the related tools are almost entirely missing ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort in providing the comments. We respond point-by-point to the concerns raised. In the strengths, the reviewer states we are solving a practical and important problem, and provide a complete theoretical framework. We kindly ask the reviewer to reconsider ... | null | null | null | null | null | null |
Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment | Accept (poster) | Summary: This study focuses on the translation of images into a sequence of discrete language tokens, aiming to enhance the understandability for language models, such as GPT-3. By achieving this, the vision-language tasks can be seamlessly translated into LLM tasks using zero-shot or few-shot learning techniques.
Str... | Rebuttal 1:
Rebuttal: Thank you for your review. We address your concerns below.
> Q1: Top1 accuracy is only best at 54%, and the model does not scale with more training data
We believe that there may be a misunderstanding. If you are referring to Figure 5, the x-axis is the masking ratio during training, and not t... | Summary: The paper proposes Language-Quantized AutoEncoder (LQAE), a representation learning approach that quantizes the latents to the codebook/vocabulary of a language model, here, a BERT model. During training, the quantized latents are masked and decoded by the frozen BERT model to implicitly impose language struct... | Rebuttal 1:
Rebuttal: We thank you for your review. We address some of your questions and concerns below.
> One motivation of LQAE is that paired data is more costly to collect, but paired data is still easy to collect at a large-scale such as LAION.
While one can source paired data from the internet such as LAION, ... | Summary: This work introduces a method for unsupervised image-text alignment named LQAE. The central concept revolves around utilizing a VQ-VAE framework, replacing the conventional codebook with frozen token embeddings extracted from a Language Model (LM). This approach aims to establish meaningful correspondences bet... | Rebuttal 1:
Rebuttal: Thank you for your review. We address your concerns and questions below.
> Are results statistically significant with one run?
We have conducted multiple runs on the few-shot prompting experiments. Across three independent runs with temperature = 0, the average scores are 29.039, 29.038, 29.039,... | Summary: The proposed approach, LQAE, addresses the lack of visual grounding in large language models by aligning text and image data in an unsupervised manner. It encodes images as sequences of text tokens by quantizing image embeddings using a language codebook and reconstructing the original input with a masked vers... | Rebuttal 1:
Rebuttal: Thank you for your review. We address your concerns below.
> The proposed method may lack explainability compared to methods that use contrastive learning to train on text-image pairs.
Interpretability is indeed an existing flaw in the method, as we do not use text-image pairs. However, we belie... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Unleashing the Power of Randomization in Auditing Differentially Private ML | Accept (poster) | Summary: The use of canaries is a very intuitive strategy to evaluate the DP mechanism (which can subsequently attract a broader non-DP specialist audience to this area). The paper is structured well, has comprehensive details on both the method and the corresponding experimental results and shows clear motivation and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the questions.
> **I would like the authors to comment on why their work is a substantial contribution given the existence of the method [of Steinke et al.]**
We make **4 key contributions**: (1) the framework of lifted DP, (2) a novel randomized leave-one-out hypothes... | Summary: The authors propose a principled improvement over existing privacy auditing techniques. Privacy auditing through the binary hypothesis testing formulation implies gathering samples on the success of the adversary when trying to correctly guess the membership game against a mechanism $M$. All auditing technique... | Rebuttal 1:
Rebuttal: We thank the reviewer for a detailed review and thought-provoking questions.
> **Bias-variance for ML pipelines + auditing with input poisoning**
We agree with the reviewer that for the Gaussian mechanism, we have all the information to (numerically) compute the bias and variance exactly for our... | Summary: This work studies the sample complexity (in terms of sampled models) of auditing differential privacy. It proposes a new approach to usage of canaries in auditing via hypothesis test, breaking an existing sample complexity barrier by making use of multiple, randomized canaries. These randomized canaries are us... | Rebuttal 1:
Rebuttal: We thank the reviewer for a constructive review.
**Additional discussion on the bottleneck of sample complexity for privacy auditing:**
Auditing DP is fundamentally constrained by the sample size $n$, where each sample corresponds to one trained model. Rigorous lower bounds on privacy require ... | Summary: This paper presents a methodology for calculating lower bounds for the parameter $\\varepsilon$ of an $(\\varepsilon,\\delta)$-differentially private (DP) mechanism. When applied to machine learning algorithms, the methodology can be used to audit the privacy guarantees of popular training algorithms such as D... | Rebuttal 1:
Rebuttal: We thank the reviewer for a thorough review and thought-provoking questions!
> **Weakness 1: ... very simple models and small datasets ...**
These are standard benchmarks in privacy auditing literature, due to computational limitations. We refer to our response at the end of this rebuttal for a... | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed and thoughtful comments. We are delighted that the reviewer appreciated the novelty, principled formal analyses, and potential impact of our work.
We are excited that the reviewers recognize that our work "solves a clearly stated problem known in the priv... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Robust Mean Estimation Without Moments for Symmetric Distributions | Accept (poster) | Summary: This paper studies outlier-robust location estimation for symmetric(-like) distributions. For "semi-product" distributions, the authors show that one can achieve $O(\epsilon \sqrt{\log 1/\epsilon})$ asymptotic error with a polynomial number of samples (and time), and $O(\epsilon)$ asymptotic error when given q... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for you feedback. We hope to address your concerns in this note. We believe your questions and this discussion form a valuable addition to our paper and we will add this.
### Knowledge of Problem Parameters
In the setting of the introduction, e.g., Theorems 1.4-1.6, our... | Summary: This paper considers sample complexity of (robust) mean estimation possibly without moments. Two typical examples are product distributions and elliptical distributions. The main technical contribution of this paper is to adapt the filtering techniques to the setting with less restrict moment assumptions.
St... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We thank you for your feedback and are happy that you enjoyed reading our paper.
### Probability Mass Around the Location Parameter vs. Concentration
While the condition that some mass is present around the location parameter, e.g., that $\mathbb{P}(\lvert \eta \rvert \leq \rho) ... | Summary: This paper studies the robust mean estimation problem without any moment assumptions. Instead, they consider a class of symmetric distributions, that is, semi-product and elliptical distributions. They develop a method based on Huber loss and the classic filtering technique that can achieve the same error rate... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your feedback. Regarding the title of our submission: Reflecting that we exploit symmetry already in the title is a good idea. For example, a candidate for the title could be "Robust Mean Estimation Without Moments for Symmetric Distributions".
### Notation
We will ... | Summary: This work studies robust mean estimation for symmetric distributions. There may possibly no moments (not even the first moment, hence the term location instead of mean), so previous efficient algorithms that rely on strong distributional assumptions may not work on the heavy-tailed setting.
The main result i... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We thank you for your feedback. We are happy that you enjoyed reading the content of our paper. We agree that including a succinct description of our algorithm, e.g., as an algorithms box, as well as a conclusion to the end of the paper would improve the presentation. We will add t... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Memory-Constrained Algorithms for Convex Optimization | Accept (poster) | Summary: The main result of the paper is a theorem that establishes a trade-off between oracle-complexity and memory-usage for convex optimization problems. More specifically they show that given a convex optimization problem in R^d, then for any p between 1 and d there is a deterministic first-order algorithm tha solv... | Rebuttal 1:
Rebuttal: Thank you for reading our paper carefully and for the positive views! For your first point, we agree that our algorithm relies heavily on Vaidya's cutting-plane method [1] and its variant proposed in [2]. We will provide more detailed comparisons between our algorithm and (the variant of) Vaidya's... | Summary: The paper studies the problem of memory constrained convex optimization. In this setting, the algorithm is given oracle access to the gradients of an unknown convex function and is tasked with finding an approximate minimizer. The additional constraint is that the algorithm is constrained to use as few bits of... | Rebuttal 1:
Rebuttal: Thank you for the positive view and we really appreciate your suggestions! We agree that our algorithm improves over gradient descent only in the regime $\epsilon\leq \frac{1}{d^{\mathcal O(d)}}$, which might be smaller than regimes considered in some previous works on oracle complexity of convex ... | Summary: This paper studies solving feasibility problems, and hence optimization problems, and focusing on the trade-offs between the number of oracle calls and use of memory. The precise problem is to find a point in a convex set inside the unit cube, given access to a separation oracle, which reports if a query point... | Rebuttal 1:
Rebuttal: Thank you for the positive view and the questions! Regarding the weakness, although our work does not provide a full understanding of the memory-query landscape, we emphasize the following three contributions.
- To the best of our knowledge, prior to this work, no positive results nor algorithms... | Summary: The authors propose an algorithm to solve a feasibility problem with constrained memory while minimizing the number of calls in separation oracle. The memory complexity and the calls to separation oracle is parametetrized by a parameter $p$. They get $O(d^2/p \log 1/\epsilon)$ bits of memory with $O(d/p\log(1... | Rebuttal 1:
Rebuttal: Thank you for your positive view of the paper. The number of oracle class indeed grows exponentially with the parameter $p$, due to the recursive nature of the algorithm. We emphasize however that these are the first class of algorithms providing a positive result to trade-off memory and oracle-co... | Rebuttal 1:
Rebuttal: In this document, we provide in Figure 1, a geometric illustration of the recursive step of our algorithm, where an approximate separating hyperplane to the father problem (in the recursive hierarchy) is computed (Reviewers U1uo and H4w2).
Pdf: /pdf/6a9cadf2970d5355796ade9aa4a6555513894a79.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper discusses a parameterized family of memory-constrained algorithms for convex optimization and compares the memory and oracle call complexity of this family with existing algorithms. The authors show that with this parameterized family, one can trade between oracle call and memory complexity, and they... | Rebuttal 1:
Rebuttal: Thank you very much for your positive view of our paper and its contributions, and for your useful remarks. We will take care to solve all minor issues in the revised manuscript. We will also make sure to clarify parts of the exposition mentioned. With respect to the conference, NeurIPS has a trad... | Summary: The authors study memory vs oracle calls trade-off for convex optimization. The examples of problems they consider are: To find a point in the $d$-dimensional unit ball up to error $\epsilon$, or to minimize $1$-Lipschitz convex functions over the unit ball up to error $\epsilon$.
They provide a family of algo... | Rebuttal 1:
Rebuttal: Thank you for your comprehensive summary and your positive view of our algorithms and analysis. We are very happy to provide some answers to the concerns you raised.
1. We believe that this work provides some important clarifications to the memory/oracle complexity landscape. We emphasize that to... | null | null | null | null |
Beyond Average Return in Markov Decision Processes | Accept (poster) | Summary: The paper investigates general functionals of the distribution over returns in three situations:
1) For policy evaluation, the authors prove some error bounds for a given distributional RL algorithm.
2) For planning, the authors exhibit the functional form that is optimizable via dynamic programming.
3) For re... | Rebuttal 1:
Rebuttal: Thank you for your supportive and insightful feedback. Please see our response addressing your concerns and questions :
- The obtained error bound for policy evaluation seems to be very loose, as also suggested by the experiments. Can we say anything about the tightness of this bound?
First,as ... | Summary: This paper considers the problem of using distributional reinforcement learning to perform policy evaluation and planning with more general classes of reward functionals than those typically considered in standard Markov decision processes and RL. The paper focuses in particular on dynamic programming methods ... | Rebuttal 1:
Rebuttal: Thank you for your supportive and insightful feedback. Please see our response addressing your concerns and questions:
> About the relevance of section 3 related to theorem 2:
This matter was addressed in the global rebuttal as it asked by several reviewers
> About the definition of Bellman Op... | Summary: Classic RL maximizes the (discounted) cumulative reward in Markov Decision Processes (MDPs). This paper studies more general functionals of rewards, such as generalized means. These more general functionals are useful to consider for certain applications, such as those with safety concerns. Furthermore, the st... | Rebuttal 1:
Rebuttal: Thank you for your in depth review of the paper and the relevant questions that will help clarify it. We now address each raised points :
- About the undiscounted setting theory :
Thank you for pointing out this sentence, we agree it was unclear. The goal of this sentence and the following was... | Summary: This paper addresses
Strengths: 1. The method is very well-presented, with notations, terms and algorithms put in a very clear and understandable fashion. They are introduced without fancy names and terms, which is great.
2. The questions are well-stated and addressed respectively from a theoretical perspecti... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment of our paper. We respond below to your specific concerns but please also check the common response which contains clarifications that were common to all reviewers and will be added to the final version.
- Discussion of Limitations, societal impact and ethic... | Rebuttal 1:
Rebuttal: **We first want to thank all the reviewers** for their constructive feedback and relevant questions that will help clarify the article. There are a few questions raised by several reviewers that we wish to address here.
### Experiments and tightness of our bound
We first want to point out **a min... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Large Language Models Are Zero-Shot Time Series Forecasters | Accept (poster) | Summary: The paper discusses a novel approach for time series forecasting by encoding time series as numerical digits and treating it as next-token prediction in text. Surprisingly, it is found that large language models (LLMs) like GPT-3 can effectively extrapolate time series even without being specifically trained f... | Rebuttal 1:
Rebuttal: Thank you for your comments. While we appreciate your feedback, we believe that based on the concerns you raise, some of the experiments we performed may have been overlooked.
**"most experiments are conducted on virtual datasets"**
We do not believe this is an accurate assessment. For time serie... | Summary: This paper investigates to which extent an off-the-shelf large language model like GPT-3 can be used for time series forecasting. Its main claim is that forecasting performance can be very good, provided some care is taken in correctly encoding the input. Some limited experiments are proposed to support the cl... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for your detailed review. We appreciate your recognition of the paper's relevance, fascinating results, and potential for impact. We apologize if the tone of our paper comes off as dismissive of prior work. We never intended this to be the case... | Summary: This paper proposes a method to use LLMs for time series prediction.
Precisely, they develop a way to tokenize and encode numerical digits and prompt LLMs to generate future numbers given the past.
Their method is comparable to or exceeds the state-of-the-art time series models.
Strengths: The paper propose... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and supportive review. Your comments about improving the readability of the paper through section 3 are well received. We have added a paragraph at the start of section 3 giving a high-level overview of the method, and we have revised the experiments in Figure 2 to us... | Summary: This paper studies the usage of large language models such as GPT-3 for zero-shot time series forecasting task. To construct the LLMs input sequence in line with how they are pre-trained, it proposes to encode the continuous time series as a string of numerical digits with spaces added between single digits. A... | Rebuttal 1:
Rebuttal: We thank you for your review and comments on the paper.
**Transformer based time series methods** As we mentioned in the general response, we have added an additional evaluation of our model using the LLAMA-2 LLM which includes comparisons against Informer, Autoformer, and FEDformer as you sugges... | Rebuttal 1:
Rebuttal: General Comment to All Reviewers
We are thankful for the thoughtful and supportive feedback. We were happy to see many of the reviewers think that the goals and results of the paper are interesting, relevant, and useful. Few ML researchers would confidently assert that language models are zero-sh... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors propose a way to use autoregressive large language models to solve time series problems. Several tricks make the time series inputs amenable to LLMs (e.g., separating digits by a space to circumvent tokenization problems). GPT-3 outperforms existing methods on several time series benchmarks. LLMs c... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful and detailed review. In our understanding, your main concerns about the paper are the need for testing additional LLMs, the lack of comparison against Informer and Autoformer, and the purpose of the argument that we present in section 3.2.
**Evaluating other LLMs... | Summary: The papers presents an analysis of larger language models for the task of time series prediction. The paper claims that these large language models being very generalizable are able to work very efficiently with numbers in these tasks since each digit prediction is a conditional over the previously predicted d... | Rebuttal 1:
Rebuttal: Thank you for the supportive review and your comments on related work.
**Comparison to related work**
Though other papers have investigated the use of LLMs in basic arithmetic (e.g Goat [1]) and forecasting (e.g. PromptCast), we believe the findings of our paper are unique in their scope and fo... | Summary: Briefly summarize the paper and its contributions. This is not the place to critique the paper; the authors should generally agree with a well-written summary.
This paper proposes that LLMs like GPT-3 can be employed as time series forecasters due to the similarities between text and time series such as period... | Rebuttal 1:
Rebuttal: Thank you for your supportive review!
To answer your questions,
We did not study the few-shot setting, but it would be a great direction for future work. We suspect that both few-shot prompting and fine tuning could substantially improve forecasting performance. One challenge of few-shot promptin... | Summary: This paper discusses a novel approach to time series forecasting using large language models (LLMs) like GPT-3. The authors encode time series as a string of numerical digits and view time series forecasting as next-token prediction in text. They find that LLMs can extrapolate time series at a level comparable... | Rebuttal 1:
Rebuttal: We thank you for your review.
**Understanding why LLMs can perform extrapolation on time series:**
We have demonstrated empirically that LLMs indeed can perform extrapolation on both deterministic and stochastic time series in Section 3.1 and 3.2. We explicitly pose the ability to extrapolate s... |
Neural Lighting Simulation for Urban Scenes | Accept (poster) | Summary: This paper proposed LightSim, a lighting-aware camera simulation system for improving robot perception. This system built relightable digital twins from real-world raw sensor data and enabled applications, e.g., actor insertion, modification, removal, and re-rendering.
Strengths: 1. Propose a complete system ... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful reviews and comments. We address the concerns as follows
**Q1: Novelty - Integration of previous works** \
**A1:** Our paper’s novelty lies in developing a neural lighting simulation system for self driving, which is critical for thorough evaluation and more robust trai... | Summary: Generating training data for self-driving cars is a challenging task due to the difficulty of capturing real-world scenarios. While video games have been used to generate training data, there exists a domain gap between virtual and real-world environments.
To address these challenges, the authors of this pape... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful reviews and comments. We are glad that the reviewer appreciates the novelty and contribution of our work. We address the concerns as follows.
**Q1: Judging illumination is hard for human without reference** \
**A1:** Thanks for the suggestion. We agree that it is challe... | Summary: This paper presents a system for decomposing urban outdoor driving scenes into estimated geometry and lighting components, which are then used as inputs to their deferred neural rendering workflow. The geometry is represented as a mesh that is extracted using Marching Cubes, from an optimized SDF volume, whil... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful reviews and comments. We also appreciate the detailed suggestions to improve the presentation quality. We address the concerns as follows.
**Q1: Discern novelty/contribution with FEGR** \
**A1**: As discussed in the related work section, FEGR is a **concurrent and indep... | Summary: The paper proposes a method, LightSim, for recovering geometry, appearance and scene lighting for driving scenes, which enables downstream applications of scene editing and lighting editing. The method incorporates a learned sky dome estimator for hallucinating the original lighting from limited observations, ... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful reviews and comments. We are glad that our work is recognized as “novel” with “noticeable qualitative improvements”. We address the concerns as follows.
**Q1: Clarifications and additional details** \
**A1:** We thank the reviewer for the feedback to improve the presen... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful reviews and valuable comments. We are excited that the reviewers found our approach “novel” [**Reviewer 5Bwf, Reviewer 25CV, Reviewer ENZ6, Reviewer 6GXP**], and acknowledged our evaluation is “thorough” [**Reviewer 5Bwf**], the results are “good/high-qu... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work extends a recent novel view synthesis approach for autonomous driving scenes with relighting and virtual object insertion with properly cast shadows. The proposed method is a system that consists of the following steps: i) geometry and albedo reconstruction from sensor data, ii) estimation of the env... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We are excited that LightSim is recognized as a “well-designed system” which is “technically sound” with “really thorough experimental evaluation” and “the design choices are well supported”. We are also glad that the reviewer believes the motivation is “very ... | null | null | null | null | null | null |
SEENN: Towards Temporal Spiking Early Exit Neural Networks | Accept (poster) | Summary: This paper considers to adaptively determine inference time steps of spiking neural networks to improve the tradeoff between accuracy and time. Two methods are proposed. The first one uses confidence score thresholding. The second one introduces an additional policy network to predict the number of timesteps b... | Rebuttal 1:
Rebuttal: Thank you for your efforts in reviewing our article and providing constructive feedback. We’d like to reply to your concerns in detail.
Q1: Dynamic inference time steps of SNNs were also explored by (possibly concurrent) recent works [1,2], which can be discussed.
A1: Thank you for pointing ou... | Summary: This paper proposes an accuracy-efficiency tradeoff method by adjusting the number of timesteps in SNNs, which is new and interesting. The authors accomplish this idea with two methods that uses a confidence score thresholding and reinforcement learning. These methods can be applied to directly training SNN an... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback on our work. Please check our response to your questions and concerns.
Q1: To show the effectiveness of the method. A similar timesteps for other methods should be provided, for example, 2 or 3 timesteps for TET on ImageNet.
A1: Thanks for the suggestion. We... | Summary: This paper introduces a new inference scheme for spiking neural networks (SNNs), the early exit on the time dimension. To make sure that relatively easy images can be predicted with less number of timesteps, this work build two frameworks to identify the appropriate timestep to minimize the latency while maint... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback on our work. Please check our response to your questions and concerns.
Q1: There is no empirical evidence showing that the confidence score will increase along with the number of timesteps. The authors are suggested to add a figure to show the confidence scor... | Summary: This paper proposes a novel manner to improve the efficiency of the spiking neural network. Specifically, the SEENN is proposed to determine the appropriate number of timesteps, which therefore reduce the inference time-cost. The proposed method is evaluated on CIFAR-10 and Imagenet, achieving good performance... | Rebuttal 1:
Rebuttal: Thank you for your efforts in reviewing our article and providing constructive feedback. We’d like to reply to your concerns in detail.
Q1: The proposed method for determining the best timesteps is not novel. The first manner is to simply set the right number with the confidence score, while the... | Rebuttal 1:
Rebuttal: We’d like to thank all reviewers for their constructive feedback and suggestions on our work. We will address each reviewer’s questions and concerns point-to-point. And we welcome any further discussion on our paper. We have attached a PDF file for demonstrating 4 figures. A detailed description ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Bridging Discrete and Backpropagation: Straight-Through and Beyond | Accept (oral) | Summary: The presented paper tackles the optimization of the parameter of the distribution of a discrete random variable through stochastic gradient descent by addressing the problem of gradient estimation given samples from the discrete distribution.
The authors consider three popular estimators as a starting point fo... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We value your comments and will address your concerns regarding the correctness of the derivation, with further elaborations to be included in the final paper.
**Reply to weakness argument 1:** Since $\sum_i \pi_i = 1$, we have $\sum_i E[f(D)] \frac{d \p... | Summary: This paper works on gradient approximation for discrete latent variables. The problem is challenging because the discreteness hinders direct backpropagation through the neural networks; hence, an approximation is needed. The authors address the issues from the perspective of a second-order approximation of the... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We value your comments and will address the concerns regarding experimental design and presentation in this rebuttal, with further elaborations to be included in the final paper.
**Reply to weakness 1:** We understand the complexity of ReinMax's exact for... | Summary: The paper proves that the straight-through (ST) gradient estimator for the categorical distribution can be viewed as a first-order approaximation of the true gradients. Based on this point of view, a new gradient estimator, ReinMax is proposed based on Heun's method which is a second-order method. Experiments ... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We value your comments and will address the concerns regarding the novelty of our study and experiment design, with further elaborations to be included in the final paper.
**Reply to weakness argument 1:** While it may be intuitive to some that the strai... | Summary: This work develops a new method, ReinMax, to compute gradients of parameters
used to generate discrete random variables; specifically, the parameters of a
multinomial's softmax parameterization. It provides a new perspective onto the
existing straight-through (ST) gradient estimator as a first-order
approximat... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We value your comments and will address the concerns regarding experimental design, presentation, and limitations of ReinMax in this rebuttal, with further elaborations to be included in the final paper.
**Reply to limitation 1:** While our main text focu... | Rebuttal 1:
Rebuttal: # General Rebuttal
We thank all reviewers for their thoughtful feedback. In this work, we tackle critical challenges in gradient estimation for discrete variables, and our contributions are notable in two primary areas:
- we formally established that the straight-through estimator is a first-orde... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper is about designing a new approach to approximating the gradient of parameters in generating discrete random variables.
The work first starts with discovering the connection of Straight-Through (ST) estimator and the first-order approximation of the true gradient.
From the insight, the authors aim to... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We value your comments and will address the concerns regarding experimental design in this rebuttal, with further elaborations to be included in the final paper.
**Reply to weakness 1:** Thanks for the suggestions! We will add more elaborations in the rev... | Summary: The paper shows that the straight-through estimator is a first-order approximation of the gradient. The authors then propose a method, called ReinMax, which provides a second-order approximation with negligible computational overhead. Experiments are performed in several settings involving discrete variables (... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We value your comments and will address the concerns regarding experimental design, applicability, and limitations of ReinMax in this rebuttal, with further elaborations to be included in the final paper.
**Reply to Weakness 1 and Question 1:** In our sub... | null | null | null | null |
Generative Evolutionary Strategy For Black-Box Optimization | Reject | Summary: The paper introduces a Generative Evolution Optimization (GEO) algorithm to black-box optimization, introducing. The GEO algorithm is claimed to combine the strengths of Evolution Strategy (ES) and Generative Surrogate Network (GSN) to address the limitations of Bayesian optimization and other existing methods... | Rebuttal 1:
Rebuttal: Thank you for your review and the many questions.
I will provide answers to each of your questions in turn.
Q1. The paper mentions the tendency of GEO to collapse towards one side while optimizing certain functions. Could you provide more insight into why this happens?
A1. In the case of ZDT2 wh... | Summary: This paper investigates a new integrated optimization method targeting at black-box optimization within high-dimensional spaces scenario, called Generative Evolutionary Optimization (GEO).
Different from the popular black-box optimization method - Bayesian optimization, GEO exhibits a linear time complexity.
I... | Rebuttal 1:
Rebuttal: Response to the Review:
Thank you for your thorough review.
First, here are the answers to the questions raised:
Q1. How many initial points are sampled in the Latin hypercube stage?
A1. The number is equal to the size of the generator pool used in the evolutionary strategy (ES). For example, i... | Summary: In this paper, a black-box optimization approach is proposed that combines an evolutionary strategy (ES) with a generative surrogate neural network (GSN) model. This integrated model is designed to function in a complementary manner, where ES addresses the instability inherent in surrogate neural network learn... | Rebuttal 1:
Rebuttal: Thank you for your review. I will respond to each question one by one.
Q1. In the introduction, there is no summary of the contribution, and I suggest that the contribution of this work be further emphasized.
A1. This issue seems to be a writing problem similar to your 3rd question. In short, th... | Summary: The paper introduces a new method called Generative Evolutionary Optimization (GEO) that aims to address the challenges of black-box optimization in high-dimensional problems. The authors highlight that existing algorithms, such as Evolution Strategies (ES) and Bayesian optimization, have limitations when it c... | Rebuttal 1:
Rebuttal:
Thank you for your review. I will address the Weaknesses and Question you pointed out one by one.
----------
W1. Some simple ES algorithms, such as OpenAI-ES [1], can optimize about 100k parameters in their paper; it is used to optimize the weight of the policy network. Although the idea of thi... | Rebuttal 1:
Rebuttal: Dear Reviewers,
I extend my heartfelt thanks for your diligent reviews. I have responded to each of your questions individually. However, given the character limit in the Rebuttal section, I was constrained to provide only brief answers to each question. Should you have any additional questions, ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Towards the Difficulty for a Deep Neural Network to Learn Concepts of Different Complexities | Accept (poster) | Summary: Theoretical work using prior work defining "interactive concepts" to quantify difficulty of concepts and show why DNNs prefer learning simpler concepts by approximating the concept learning process for DNNs by linear regression.
Strengths: 1) Useful theoretical contribution that clearly uses interactive conc... | Rebuttal 1:
Rebuttal: Thank you for your great efforts. We are glad that all reviewers have given us positive comments. We will try our best to answer all your questions.
Please let us know if you still have further concerns, or if you are not satisfied with the current responses, so that we can further update the resp... | Summary: This paper explores the problem of the learning difficulty of interactive concepts. The paper theoretically shows that a DNN is more likely to encode simple interactive concepts (with fewer variables in the interaction). Low-order interactive concepts are more stable to data noises and they exhibit consistent ... | Rebuttal 1:
Rebuttal: Thank you for your great efforts on the review of this paper. We will try our best to answer all your questions.
Please let us know if you still have further concerns, or if you are not satisfied with the current responses, so that we can further update the response ASAP.
---
Q1: "Authors claim... | Summary: In this paper, the authors proved several theoretical results that formalized the idea that simple interactive concepts achieve a smaller variance in their interaction effects in the face of Gaussian perturbations, and are thus more stable and easier for deep neural networks (DNNs) to learn. The authors also p... | Rebuttal 1:
Rebuttal: Thank you for your great efforts on the review of this paper. We will try our best to answer all your questions. Please let us know if you still have further concerns, or if you are not satisfied with the current responses, so that we can further update the response ASAP.
---
Q1: Ask how to use t... | Summary: The paper provides theoretical results explaining why simpler concepts are easier to learn for neural networks. The paper refers to prior work on interactive concept models, which define a concept as a subset of input features, and defines the complexity of each concept as the number of features contained with... | Rebuttal 1:
Rebuttal: Thank you for your great efforts on the review of this paper. We will try our best to answer all your questions. Please let us know if you still have further concerns, or if you are not satisfied with the current responses, so that we can further update the response ASAP.
---
Q1: Ask for the theo... | Rebuttal 1:
Rebuttal: Thanks for all reviewers' great efforts and comments. This paper has received rating of a *strong acceptance*, a *weak acceptance*, and two *borderline acceptance*. We are glad to answer all your questions and conduct **new experiments as requested.**
**Please let us know if you still have furthe... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Class-Conditional Conformal Prediction with Many Classes | Accept (poster) | Summary: Standard conformal prediction provides a marginal coverage guarantee which is insufficient for many practical applications. Class-conditional conformal prediction is suitable for many applications, especially in $Y \rightarrow X$ settings, e.g., image data. Achieving class-conditional coverage relies on learni... | Rebuttal 1:
Rebuttal: Thank you for your review! We appreciate your summary of the motivation for achieving class-conditional coverage and why it can be difficult in multi-class classification problems. Thanks for suggesting we cite Sadinle et al., 2016, which presents a variant of classwise conformal. We have added it... | Summary: This article studies the clustered conformal prediction, mitigating the issues in the standard conformal and class-wise conformal, by grouping some similar classes together.
Strengths: * The authors proposed clustered conformal prediction to strike the balance between marginal coverage and class-wise coverag... | Rebuttal 1:
Rebuttal: Thank you for your review and questions.
In response to weaknesses:
> W1: “Low-data scenario”; cluster-conditional conformal vs. class-conditional conformal
We are not aware of other works that focus on creating prediction sets that target class-conditional coverage in the many-class regime. ... | Summary: The paper studies how to achieve class-conditional coverage (in the setting of conformal prediction) for multiclass classification problems, in particular for tasks with large label spaces. Previous techniques either provide no class-wise guarantees or tend to produce conservative prediction sets due to lack o... | Rebuttal 1:
Rebuttal: Thank you for your nice summary of the paper!
In response to weaknesses:
> W1: Inducing artificial clusters
Our method does, in a sense, induce artificial clusters, but we do not view this as a weakness. In the original datasets, there is no true clustering. We simply partition the classes in ... | Summary: The paper describes a method for conformal prediction specially addressed for the case where there is few avaliable data in some of the classes. To address this problem, the proposed method performs a clustering of classes with similar score distributions with the goal of increasing the data used to select the... | Rebuttal 1:
Rebuttal: Thank you for your review and, in particular, for highlighting our practical recommendations, which we hope will provide useful guidance for practitioners.
In response to weaknesses:
> W1: Classes with few examples
See our common response, which describes additional experiments we ran to look... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for engaging with our paper and for providing helpful comments and suggestions. We have incorporated the feedback into the paper, and have run a new set of experiments (described below).
The reviewers agreed the paper was well-written and that we thoroughly v... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Towards Faithful Sign Language Translation | Reject | Summary: The paper proposes a novel framework for sign language translation (SLT), which can integrate multiple SLT subtasks. The work is motivated by a series of experimental analysis, e.g., converging speed of different subtasks and relationship between SLT and sign language recognition (SLR) performance. Besides, tw... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and constructive comments. Our review responses are summarized below.
**Q6. About the choice of evaluation metrics.**
Thanks for your constructive comments on the evaluation metrics. As we mentioned **in the “global” response**, we suggest evaluating the over... | Summary: This paper mainly discusses the challenges of improving faithfulness in sign language translation and proposes solutions. The researchers leverage rich monolingual data and adopt back-translation to generate synthetic parallel data, explore the potential of denoising auto-encoder, and propose the MonoSLT frame... | Rebuttal 1:
Rebuttal: Thanks for the insightful comments. Our responses to them are summarized below.
**Q1. About the non-manual signals.**
We agree with you that non-manual signals are essential for SLU. One of the major reasons that we select keypoints as input is that synchronous signals can be easily modeled by ... | Summary: The paper discusses the issue of faithfulness in sign language translation (SLT), which refers to whether the SLT model captures the correct visual signals. It is found that imprecise glosses and limited corpora can hinder faithfulness in SLT. To address this, the paper proposes MonoSLT, which integrates SLT s... | Rebuttal 1:
Rebuttal: **Q1. About the bug-free evaluation on the CSL-Daily dataset.**
Thanks for your suggestion, it pushes us to figure out the performance gap between MMTLB[12] and the used baseline. We present the re-evaluated results **in the "global" response**. Moreover, we also find that the overconfident predi... | Summary: This work is dedicated to enabling the SLT model to capture correct visual signals (faithfulness in SLT). In order to improve faithfulness in SLT, the author integrates SLT subtasks into a single framework named MonoSLT, and based on this, proposes alignment constraints and consistency constraints. The former ... | Rebuttal 1:
Rebuttal: **Q1. About the inconsistent punctation bug.**
Thank you for bringing this up. During experiments, we find the tokenization process of the original implementation always predicts punctuation marks in English, which greatly affects the evaluation results, especially for longer n-gram. For example,... | Rebuttal 1:
Rebuttal: **Thanks for the constructive suggestions and insightful comments from all reviewers, they push us to think about the faithfulness in SLU more deeply. Our review responses for common questions are summarized below.**
**Q1. Potential quantitative metrics for faithfulness**
Inspired by previous wo... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work mainly studies the faithfulness issue in SLT (i.e., whether the SLT model captures correct visual signals). The study identifies imprecise glosses and limited corpora as the main factors contributing to limited faithfulness. In order to mitigate this issue, this work proposes a framework called MonoS... | Rebuttal 1:
Rebuttal: **Q1: About the further clarification on the quantitative metrics.**
Thanks for the insightful comment about the metric, and it pushes us to think about the evaluation metrics of faithfulness more deeply. As we mentioned **in the “global” response**, we suggest evaluating the over-translation and... | null | null | null | null | null | null |
Reining Generalization in Offline Reinforcement Learning via Representation Distinction | Accept (poster) | Summary: Authors consider overgeneralization in offline RL which is new and propose the modification which helps to solve the problem and can be applied to any algorithm.
Strengths: Proposed method boosted performance of different algorithms by a notable margin in most of the cases and it can be integrated in any alg... | Rebuttal 1:
Rebuttal: ### run more experiments using AntMaze or all of the Adroit datasets
As AntMaze datasets are designed to investigate the stitching ability and the datasets can well cover the state-action space, our approach may not help significantly. Therefore, we perform experiments on Adroit datasets. We repo... | Summary: The authors investigated the effect of generalization in overestimation of critic values and devised an algorithm that can mitigate overgeneralization. The proposed method, Representation Distinction (RD), is orthogonal to most offline RL algorithms and thus be applied to most of them. Experimental results sho... | Rebuttal 1:
Rebuttal: ### pseudocode of CQL+RD will be helpful.
Here we provide the detailed illustration of modified CQL based on the insight of RD. The core idea of CQL conservativeness is to increase the Q value of the data in the dataset $\mathcal{D}$ and diminish the Q value of the data generated by a mixed poli... | Summary: This paper explains the necessity of reining generalization in offline RL and proposed a new method Representation Distinction (RD) based on this principle. RD shows good performance on D4RL benchmarks.
Strengths: - This paper presented a view called Backup-Generalization Cycle, which explains that the gener... | Rebuttal 1:
Rebuttal: ### gap between analysis and practical implementation
The conceptual illustration of reining generalization via kernel control in Section 4.3 including two stages: Policy-Dataset generalization inhibition and Policy-OOD generalization inhibition. In the practice algorithm design, we design loss ... | Summary: The paper proposes a flexible plug-in method called Representation Distinction (RD) to address the overgeneralization issue in offline Reinforcement Learning (RL) algorithms. The authors formalizes the process of generalization and investigates the potential to rein the generalization from the representation p... | Rebuttal 1:
Rebuttal: ### analyses on how RD and other existing NTK-relevant approaches address generalization issues in offline RL
See the global rebuttal parts.
### RD via kernel control is doing value regularization implicitly on representation level
The core idea of RD is to encourage the learned Q-function to y... | Rebuttal 1:
Rebuttal:
### analyses on how RD and other existing NTK-relevant approaches address generalization issues in offline RL
RD v.s. DR3 [1]
- RD and DR3 differ at both the theoretical framework from which they are derived and the regularization effect in terms of out-of-sample data. DR3 is derived from the th... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning | Accept (poster) | Summary: The authors propose a novel probabilistic meta-learning algorithm called HTGM
which models the full hierarchical sampling procedure in few-shot classification
in order to perform prediction and novel task detection. To learn the parameters
they employ the EM procedure together with variational inference. Exper... | Rebuttal 1:
Rebuttal: Thank you so much for the constructive feedback. Here are our responses.
Q1. Limitations of disregarding the partition function
Response: The main issue of replacing the partition function with its upper bound is that it increases the noise in the inferred class mean. Note that the entropy of di... | Summary: This paper studies the task distribution in meta learning and proposes modeling the task in multimodal distributions. To enable efficient modelling and inference, the author develops the hierarchical Gaussian mixture task generative mode and optimizes the meta learning model in expectation maximization. The pe... | Rebuttal 1:
Rebuttal: Thank you for helping us refine the work. Before answering the questions, please allow us to clarify our problem setting.
In this work, we aim to develop a robust meta-learning model for mixture task distribution such that when a meta-testing task (e.g. few-shot classification) comes, the model ca... | Summary: This paper proposes a Hierarchical Gaussian Mixture method as a means to parameterize the task generation process in meta-learning. The authors suggest that their proposed model can effectively fit a mixture of task distributions and evaluate the scoring of testing tasks. The effectiveness of the proposed meth... | Rebuttal 1:
Rebuttal: Thank you for your review. Here are our feedbacks to the review:
Response to Weakness 1: In this paper, the authors present an application of the Hierarchical Gaussian Mixture model to the meta-learning setting. While the paper provides ideas into utilizing this model for meta-learning, the metho... | Summary: In realistic scenario, training tasks and test tasks may come from different distributions. However, most existing meat-learning methods do not take it into account. Even if some do, they do not jointly handle the detection of novel tasks.
The authors propose a metric-based meta learning model that handles b... | Rebuttal 1:
Rebuttal: Thank you so much for the constructive feedback. We sincerely appreciate your valuable suggestions and questions. The following are our responses.
Q1.I am a bit confused with Eq.(4)-(5). Here, the objective of the additional negative sampling loss is to make the different class means disperse. To... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely appreciate your valuable comments that help us refine our work. If you have more questions or concerns about our response or current draft, please let us know. We are happy to discuss with you. We have uploaded a PDF file that contains figure and addtitional theoretic... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural Language Understanding | Accept (poster) | Summary: The authors formulate soft-prompt tuning as maximising mutual information between prompts and model parameters/encoded representations. The prompt is trained to maximise mutual information with the language model head parameters, and the representation of a task encoding from the model. The authors primarily t... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments!
Weakness 1 & Q1 & Q2: ***Comparisons with popular parameter-efficient finetuning (PEFT) methods***
Response: Thanks for your valuable suggestion. Below, we have introduced the Info-LoRA, which outperforms several other PEFT methods (e.g., adapter[4], BitFit[5])... | Summary: This work focuses on the initialization inefficiency of soft prompt tuning. Crucially, soft prompt tuning is notoriously challenging to obtain a good initialization, and highly sensitive to some hyper-parameters, leading to some optimization difficulty especially in low-resource scenarios. In terms of this, th... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments!
Weakness 1: ***your task settings are somewhat less interesting given a lot of works on better discrete prompt optimization with significant better performance under few-shot setting***
Response:
We would like to politely contend that the discrete and (vs.) sof... | Summary: This paper introduces InfoPrompt: an information theoretic framework for soft prompt tuning. It does so by introducing two additional loss terms: the head loss (maximizing prompt similarity with the LM head) and the representation loss (maximizing prompt similarity with the encoder's last hidden state).
The au... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments!
Weakness 1: ***Baselines***
Response: Thanks for the suggestion. We further include the comparison between our approach and a new baseline LoRA, as below.
| Full-data | CoLA | RTE | MRPC | SST2 | RE | NER | SemEval | Average |
|---|---|---|---|---|---|---|---|... | Summary: The paper proposes a new formula that models soft prompt tuning as maximizing mutual information between prompts and other model parameters. In order to improve the initialization of the prompts and to learn sufficient task-relevant information from prompt tokens, this paper develops two novel mutual informati... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments!
Weakness 1 & Q3: ***hyper-parameters ablation studies and the importance of the number of prompt tokens***
Response:
Similar to previous works [2, 3], we empirically choose the current hyper-parameters (without using the validation data). Specifically, based o... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification | Accept (poster) | Summary: The paper introduces a new approach called Few-Shot Weakly Supervised Learning for Pathology Whole Slide Image classification (FSWC), which aims to classify bags and instances within a WSI with only a limited number of labeled bags. The proposed solution utilizes a large language model, GPT-4, and prompt learn... | Rebuttal 1:
Rebuttal: **Q1**. Meaning of Shot and Division of the dataset.
**Response**. In Few-shot WSI classification, "shot" refers to the number of labeled slides. For publicly available datasets, we follow official guidelines to split the data into training (including validation) and testing sets. In-house datase... | Summary: This work proposes TOP - a framework using Vision-Language models for few shot Multiple Instance Learning on pathology datasets. TOP uses features generated by passing natural language prompts through a LLM to guide the pooling process for the bag level aggregation in MIL. Results show improvements over other ... | Rebuttal 1:
Rebuttal: **Q1**. performance comparison is biased.
**Response**.
Firstly, "training five times with non-fixed labeled bags" in the original text means random selection of different labeled bags in each run based on shot count to compose the training set. The test set is pre-divided and remains unseen du... | Summary: The authors introduce a two-level prompt learning framework for label-efficient classification of WSIs using pretrained visual language encoders. The main novelty of the proposed method seems to be that at the instance-level, domain-knowledge + GPT-4 guided prompt prototype groups are used to guide pooling of ... | Rebuttal 1:
Rebuttal: **Q1**. Using the median versus the max.
**Response**.
Firstly, "training five times with non-fixed labeled bags" in the original text means random selection of different labeled bags in each run based on shot count to compose the training set. The test set is pre-divided and remains unseen dur... | Summary: This paper introduces an innovative problem setting known as Few-shot Weakly Supervised WSI Classification (FSWC), wherein the availability of labeled WSIs is severely limited. In order to address this challenge, the authors propose a novel Two-level Prompt Learning MIL framework, named TOP, which leverages th... | Rebuttal 1:
Rebuttal: **Q1 & Q3**. Novelty compared with MI-Zero.
**Response**.
Our approach differs significantly from MI-Zero [1] in the following ways:
*a)* MI-Zero focuses on training a large model like CLIP for instance-level or slide-level zero-shot transfer and classification, while we leverage CLIP as a ba... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for your valuable comments. We first reply to questions raised by multiple reviewers and then other questions from every reviewer. We have uploaded all seven tables into a PDF file, and we sincerely appreciate your downloading and reviewing them.
**Q1**. Average A... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Scaling Open-Vocabulary Object Detection | Accept (spotlight) | Summary: The paper extends previous OWL-ViT detectors with self-training an 10B Web image-text data. The authors design different self-training strategies, such as token dropping, instance selection and Mosaics augmentation. It is an achievement to beat all previous methods on open-vocabulary benchmarks.
Strengths: - ... | Rebuttal 1:
Rebuttal: Thank you for your review. Below, we address your questions and comments.
## Response to Weaknesses
**_GLIP2 performs better than OWL-ST on the ODinW dataset, can the authors explain the possible reasons?_**
GLIP2 uses much more human-annotated data than OWL-ST. Specifically, OWL-ST only uses Ob... | Summary: The paper proposes a self-training recipe for open-vocabulary object detection that leverages weak supervision in the form of image-text pairs from the Web. The authors identify three key ingredients for optimizing the use of weak supervision for detection: choice of label space, filtering of pseudo-annotation... | Rebuttal 1:
Rebuttal: Thank you for your review. Below, we address your questions and comments.
## Response to Weaknesses
We are happy to confirm that we will make checkpoints and code publicly available. We hope that it will be useful as a foundation for downstream tasks.
## Response to Questions
We see our work as ... | Summary: This paper presents OWLv2 model and OWL-ST self-training recipe for open-vocabulary object detection. The detection data is greatly enriched with the aid of self-training. Concretely, the authors use WebLI dataset as the source of weak supervision for self-training. The dataset consists of approximately 10B im... | Rebuttal 1:
Rebuttal: Thank you for your review. Below, we address your questions and comments.
## Response to Weaknesses
1. **_Typos:_** Thank you for identifying these typos, we will fix them and do an additional round of proof-reading.
2. **_Discussion of noise in self-training:_** In the paper, we already discuss... | Summary: This work adopts a self-training strategy to generate web scale object annotations for open-vocabulary object detection. It uses an existing open-vocabulary object detector (OWL-ViT) to annotate 10B image-text pairs, and uses the annotated dataset for self-training. A various architecture is also proposed to ... | Rebuttal 1:
Rebuttal: Thank you for your review. Below, we address your questions and comments.
## Response to Questions
1. **_Is dropping the decoder inefficient?_** Our decoder-free architecture is actually more efficient than an equivalent encoder-decoder architecture like DETR. The number of encoder output tokens ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their useful feedback and comments.
The reviews were unanimously positive.
In our response, we address the remaining questions and concerns.
We provide an overview below and detailed responses to the individual reviewers.
In particular:
1. We confirm that we will relea... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Slimmed Asymmetrical Contrastive Learning and Cross Distillation for Lightweight Model Training | Accept (poster) | Summary: This paper proposed a new method to enable effective contrastive learning (CL) on the lightweight encoder without a mega-size teacher model, which can thus reduce the training cost. More specifically, it expanded the target model to a larger model, which shares its weights with the target model. Then the CL pr... | Rebuttal 1:
Rebuttal: ## Response to Reviewer 8Hzi
Thank you for your thorough review on our paper and we appreciate your positive feedback.
### Weakness
**Weakness 1:** Although the authors claim Ref [25] cannot work on the mega-size encoder, it should be compared with the proposed method on the lightweight encoder... | Summary: This paper proposes a self-supervised contrastive learning method to improve the light-weight network performance and reduce the training cost. It mainly consists of two components: slimmed asymmetrical contrastive learning (SACL) and cross-distillation (XD), which are able to train the efficient network from ... | Rebuttal 1:
Rebuttal: ## Response to Reviewer J8Hb
Thank you for your thorough review on our paper and we appreciate your positive feedback.
**Weakness 1:** The SACL+XD seems to apply the slimmable neural network[ref-1] to Disco[15]. Therefore, the novelty seems to be limited.
**Response to Weakness 1:** Thank you f... | Summary: This paper introduces a self-supervised contrastive learning algorithm designed specifically for training lightweight models, eliminating the need for a large teacher model. The algorithm comprises two main components: slimmed asymmetrical contrastive learning (SACL) and cross-distillation (XD). The authors ev... | Rebuttal 1:
Rebuttal: ## Response to Reviewer UkVS
### Weaknesses
Thank you for your thorough review on our paper and we appreciate your positive feedback.
**Weakness 1:** To enhance the clarity and understanding of the proposed methods, it would be beneficial to provide additional intuition. Specifically, more ins... | Summary: The authors propose a combination of two method: Slimmed Assymterical Contrastive Learning(SACL) , and Cross-Distilation(XD) with a correlation-maximization loss. SACL does magnitude pruning of filters at each epoch, and the pruned model is used as a encoder of one of two views used in the contrastive loss. ... | Rebuttal 1:
Rebuttal: ## Rebuttal to Reviewer CDjk
Thank you for your thorough review on our paper and we appreciate your positive feedback.
### Weaknesses
**Response to Weakness 1**: Thank you for pointing this out. To address your comments, we extended the experiments in Table 6 of the original manuscript with ex... | Rebuttal 1:
Rebuttal: Dear Reviewers and AC,
We thank all the reviewers for their helpful feedback, comments, and questions. As mentioned in the individual rebuttal thread, we upload the 1-page PDF attachment that includes all the updated tables for your reference.
Please let us know if you have any further questio... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More | Accept (poster) | Summary: This paper studies the robustness certification for equivariant tasks. Specifically, the adversarial robustness with group equivariance is defined in terms of the input and output distance. Then equivariance-preserving randomized smoothing is introduced as well as various smoothing schemes. Experiments show t... | Rebuttal 1:
Rebuttal: Thank you for your review!
## Baselines and the deficiencies of prior work
We discuss this aspect in detail in the global rebuttal comment above.
In short: We did not mean to say that existing certification procedures had some weakness that needed to be resolved in order to obtain stronger guara... | Summary: The paper investigates adversarial robustness for group equivariant tasks. To this end, the authors propose a novel notion of adversarial robustness:
An attacker aims to find a perturbation $x'$ "close" (up to some distance constraint in the input space) to a natural input $x$. For this perturbation, they aim ... | Rebuttal 1:
Rebuttal: Thank you for your review!
Please note that we cannot update the manuscript during the rebuttal period. We will however make several changes for the camera ready version, some of which are already integrated into the one-page pdf attached to the global comment above.
## Adversarial attacks
As yo... | Summary: In this paper, it propose equivariance-perserving randomized smoothing to provide the robustness of the GNN model. The proof demonstrates the provably robustness of the proposed smoothing method.
Strengths: 1. The theoretic guarantee of this paper is strong. And the proposed random smoothing methods can be ... | Rebuttal 1:
Rebuttal: Thank you for your helpful review, based on which we will add additional information to the camera ready version, as detailed in our discussion below.
## Experiments with different architectures
Following your comment, we performed experiments with additional geometric models for molecule data. T... | Summary: Authors note that the definition of adversarial robustness needs to be adjusted in cases when the problem is equivalent - for example, graph perturbations that are large in terms of the number of removed/added edges might actually be small if we take into account graph isomorphism (eg if a large graph perturba... | Rebuttal 1:
Rebuttal: Thank you for your review.
In the following, we would like to highlight the ways in which our results are both surprising and practically useful. We would further like to illustrate this by constrasting them with those of Karras et al. Then, we would like to call attention to the fact that the re... | Rebuttal 1:
Rebuttal: While we have already personally responded to each of the reviewers insightful comments and questions, there are two points that appeared multiple times.
We would like to use this comment to discuss them in detail. We also attached a pdf with additional figures and tables. We further list prospe... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents several contributions to the field of adversarial robustness for group equivariant tasks. The authors propose a sound concept of adversarial robustness for these tasks and demonstrate that using equivariant models can facilitate achieving provable robustness. They also prove that various ra... | Rebuttal 1:
Rebuttal: Thank you for your review. We are glad to hear that you find our paper sound and innovative.
We are not quite sure if we interpreted your first question correctly, so please let us know if you would like any additional clarification during the second part of the rebuttal period.
## Flaws of clas... | Summary: This paper provides a new insight into adversarial robustness for equivariant tasks (like graph classification) where we need to separate the perturbations that occur according the allowable transformations (that actually form a group) - and where we'd like to have the same output disregarding the norm of pert... | Rebuttal 1:
Rebuttal: Thank you for the thorough review of our submission and pointing out the interesting connection to smoothing over multiplicative transformation parameters.
## How randomized smoothing over multiplicative parameters fits into the proposed framework
We provide an in-depth discussion of transformati... | null | null | null | null |
Efficient Testable Learning of Halfspaces with Adversarial Label Noise | Accept (poster) | Summary: This paper studies the problem of learning halfspaces under Gaussian distribution and adversarial label noise in the testable learning framework of Rubinfeld and Vasilyan (STOC'23). In this setup, the learning algorithm, given a few training examples drawn i.i.d. from some unknown distribution $\mathcal{D}$, m... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the positive assessment of our work.
First, regarding the limitation on the error guarantees (does not achieve OPT but only O(OPT)), we remark there is strong evidence that achieving error $OPT + \epsilon$ in fully polynomial time under agnostic learning, ... | Summary: Learning halfspaces is a very well studied problem in machine learning. In the agnostic (adversarial label noise) and distribution free setting, this problem has been known to be computationally intractable. As a result, there have been several works of agnostic learning in distribution specific settings (wher... | Rebuttal 1:
Rebuttal:
Regarding practicality: The testable learning framework is a new learning paradigm whose definition (in a paper that appeared in STOC’23) was motivated by practical considerations (namely, the fact that one cannot verify the output of known distribution-specific agnostic learners). Since its intr... | Summary: This paper considers computationally efficient learning of halfspaces with adversarial noise in a recently proposed "testable" task: with high probability either reports the example distribution is not standard Guaissian, or outputs a halfspace with error of at most O(opt)+epsilon. There are existing works in ... | Rebuttal 1:
Rebuttal: The reviewer mentions the result is itself not surprising. While we respect this opinion, we want to point out that there are known hypotheses classes where there are separations between testable learning and standard agnostic learning. In particular, as shown in the initial work of [RV23], there ... | Summary: The paper studies the problem of efficient testable learning of half spaces under Gaussian marginals and adversarial noise. The setting of testable learning addresses the issue of restrictive assumptions on the marginal distributions (such as Gaussianity) under learning algorithms are designed. Traditionally, ... | Rebuttal 1:
Rebuttal:
Regarding the concern that our tester-learner may only work under Gaussian marginals, we refer the reviewer to our General Response.
The reviewer brought up the question of which part of the algorithm can be generalized to work under a broader family of distributions. Just as the reviewer guesse... | Rebuttal 1:
Rebuttal: ## General Response
We thank the reviewers for their time and effort in providing feedback. We are encouraged by the positive comments from reviewers for the following: (i) the **improved running time for testable learning halfspaces** from $d^{ \text{poly}(1/\epsilon) }$ to fully polynomial in ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Theoretically Guaranteed Bidirectional Data Rectification for Robust Sequential Recommendation | Accept (poster) | Summary: Overall comment:
This paper proposes two detect and rectify methods to solve the problem of unreliable instances in Sequential Recommender Systems (SRSs), including unreliable items in the input and unreliable targets. The two methods can be applied to existing SRSs models, effectively improving the model perf... | Rebuttal 1:
Rebuttal:
We sincerely appreciate your appreciation on our work and your valuable reviews.
> Question and Limitation: the paper does not discuss the limitations of the methods. In particular, I am interested in whether this method can be applied in large-scale real-world recommender systems. Are there any... | Summary: This paper aims to address the issue where the performance of sequential recommender systems (SRSs) could be harmed by mismatched input-target pairs introduced by distracted users. The paper claims that existing studies only focus on either unreliable inputs or targets but not both simultaneously. Also no theo... | Rebuttal 1:
Rebuttal:
We appreciate your recognition of our work and your valuable feedback.
> Weakness 1
Thank you for the interesting and insightful suggestions.
Constructing a synthetic dataset with ground truth for data reliability is indeed of importance for robust SRSs. However, the major challenge is how t... | Summary: This paper proposes a bidirectional data rectification framework, called BirDRec, to address the challenges of training sequential recommender systems (SRSs) with unreliable input and targets. The authors provide two theoretically guaranteed error-bounded strategies to rectify the data, which can be flexibly i... | Rebuttal 1:
Rebuttal:
Thanks for your reviews.
> Weakness 1
We exactly follow the scientific research standards and common practice in SRSs, that is, selecting (around 3~6) representative benchmark datasets that are widely adopted by SOTAs [1, 2, 4] for evaluation. We have tried our best to cover various domains ran... | Summary: The authors proposed Bidirectional Data Rectification (BirDRec) framework that can be incorporated into existing Sequential recommender systems (SRSs) to tackle unreliable data. In addition, the authors also adopt rectification sampling strategy.
The authors also provided theoretical guaranteed for BirDRec an... | Rebuttal 1:
Rebuttal:
Thank you for your invaluable comments.
## Weaknesses
> W1
The possible reasons are twofold: the item-wise correction process of STEAM, and the longer interaction sequences on ML-1M (see Tab 1).
To correct each instance, STEAM selects one type of operation from ‘keep’, ‘delete’, and ‘insert’... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper focuses on the inconsistency between user’s true preference with interaction history. Specifically, the authors propose a theoretically guaranteed data rectification strategies based on SRS predictions to tackle both unreliable input and targets for more robust SRS. Besides, the authors also devise ... | Rebuttal 1:
Rebuttal: Thanks for your invaluable reviews.
## Weaknesses
> W1
We did have the formulation of the Self-ensemble mechanism (see lines 238-245).
> W2
Thanks for bringing up the clarity issue with Fig 3(a). The table below shows the per-epoch time cost (seconds) of SASRec and BirDRec:
| | ML-1M | Beaut... | null | null | null | null | null | null |
Provably Fast Convergence of Independent Natural Policy Gradient for Markov Potential Games | Accept (poster) | Summary: This work proposes an analysis of the independent natural policy gradient algorithm for Markov Potential games. Under technical assumptions on a sub-optimality gap problem dependent quantity and supposing access to exact (averaged) advantage functions, this paper provides a novel $O(1/\epsilon)$ iteration comp... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed assessment and constructive feedback on the paper, with both main paper and appendix. We are encouraged by the fact that the reviewer finds our paper "well-organized" with "new and interesting analysis". Our submission has been revised to include previously m... | Summary: The paper provides a new analysis for the (Markov) potential games with a $O(1/\epsilon)$ convergence rate. The new results are problem-dependent and may be a tighter guarantee for certain classes of potential games. Asymptotic guarantees are also provided to elaborate on the problem-dependent nature of the re... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments and suggestions concerning our paper. Please see our response below with respect to the specific comments and we sincerely hope that the reviewer would consider increasing the score.
**Q1.** "The new results do not seem to be directly comparable t... | Summary: The article considers what the authors describe to be “natural policy gradient” learning in normal form and Markov potential games. This is the discrete time algorithm in which individual players' mixed strategies are multiplied by a softmax response to the opponent mixed strategies then normalised. Convergenc... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and support of our paper as well as the valuable suggestions. We are encouraged by the fact that the reviewer finds our paper "nicely written", with "nice convergence rate results", andthat it is "self-contained". Please see our response below with respect to the... | Summary: This paper studies the convergence of Natural Policy Gradient method (NPG) in Markov Potential Game. Under stronger assumptions, the convergence result improves upon previous ones.
Strengths: Under the considered setting, NPG is shown to achieve a $1/K$ convergence rate for multi-agent Markov Potential Game, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback regarding the paper. Please see our responses below with respect to the specific comments. We believe that we have addressed all the concerns raised by the reviewer, and we sincerely hope that the reviewer would consider increasing the score.
**Q1... | Rebuttal 1:
Rebuttal:
## Authors' Response to All (Discussion on $c$ and $\delta^*$):
We wholeheartedly thank all the reviewers for their time and their constructive feedback on our paper. The reviewers' comments provided us with great insights on how to increase clarity and reduce potential confusion about our paper... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Motion Refinement for Unsupervised Face Animation | Accept (poster) | Summary: This paper anaylizes the limitations of existing face animation methods in capturing the finer facial motions, and hence design a non-prior-based motion refinement approach to achieve finer face motion transfer in local areas. A correlation volume between the source and driving images is constructed as non-pri... | Rebuttal 1:
Rebuttal: ## Response to Reviewer zuqN
### Q1: No obvious improvements can be seen given the video results on the self reconstruction.
On one hand, we will rearrange the video results to form a nine-square presentation for better comparison. On the other hand, we suggest zooming in our Figure 4 in the mai... | Summary: This paper proposes a method for face animation via learning to refine a coarse motion field. The refinement is performed in a recurrent manner using the previous iteration's motion, occlusion map and structure correlation volume. Both the qualitative and quantitative results show improvement over prior art.
... | Rebuttal 1:
Rebuttal: # Response to Reviewer SbHP
### Q1: Identity shift during animation, and FaceIDLoss
Thanks for the good suggestion. We have compared the common face identity preservation metric CSIM in the **common response**, and our method performs slightly better on this metric while much better on the motio... | Summary: This paper contributes to the field of unsupervised face animation by introducing a novel motion refinement method to overcome the limitations of existing prior-based motion models, especially in estimating detailed facial movements.
The paper's approach introduces a new method which uses a structure correlat... | Rebuttal 1:
Rebuttal: ## Response to Reviewer pDRj
### Q1: VoxCeleb dataset may not be sufficient for evaluating finer facial motion and higher resolution dataset may be more appropriate.
Thanks for the suggestion, but we may not agree that the VoxCeleb dataset may not be sufficient for evaluating finer facial motio... | Summary: The paper presents an unsupervised method for learning to create continuous video animations of faces given a single input image and a driving video of the same or a different subject. The method relies on the optical flow between the driving and source images to warp the features of the source image, which ar... | Rebuttal 1:
Rebuttal: ## Response to Reviewer mUhK
### Q1: Some design choices are not empirically or experimentally justified in the paper. For example, why are specifically 6 iterations used to update the initial coarse estimations!
Thanks for the careful review and sorry for the confused understanding. We set the it... | Rebuttal 1:
Rebuttal: ## Common Response
We thank all Reviewers for the valuable feedback and insightful comments. We appreciate the reviewer's positive comments regarding the novelty and the effectiveness of our method. We now clarify the reviewer's common questions as follows.
### Q1: Difference with optical flow ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: 1. This work proposed a new unsupervised face animation approach to learn simultaneously the coarse and finer motions.
2. The results outperformed the state-of-the-art methods on two representative datasets.
Strengths: 1. The target task is important.
2. The method is intuitive and reasonable.
3. The results... | Rebuttal 1:
Rebuttal:
## Response to Reviewer 2aeH
### Q1: Will the increasing number of keypoints of the local affine motion model approach the non-prior based motion model? And why the proposed model works better than a local thin-plate-spline motion model?
* Thanks for the good question. If one can learn the dense... | Summary: The paper presents a new approach to generate human face videos based on a source image and a driving video, which simultaneously learning both coarse and finer facial motions. The proposed approach utilizes a structure correlation volume constructed from keypoint features to provide non-prior-based motion inf... | Rebuttal 1:
Rebuttal: ## Response to Reviewer ELv3
### Q1: Presentation of Fig.1, cross reference of Fig.3 (line 180), composing type of supplementary videos.
* Sorry for the confusion. The affine transformation is for illustration purposes, not from real examples. But the two motion flows are influenced by a pair of... | null | null | null | null |
Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond | Accept (poster) | Summary: The main contribution of this paper is a new measure called label informativeness that characterizes how much information a neighbor’s label provides about a node’s label. The paper also discusses the expected properties of homophily. The experimental results are based on the relation between the performance o... | Rebuttal 1:
Rebuttal: Thank you for the review and comments! In this response, we address your questions and concerns.
**Contribution of our work**
In our discussion below we argue why we think that LI is a novel graph characteristic and how it relates to known concepts in information theory. However, in this discuss... | Summary: This paper analyzes homophily measures and proposes a new characteristic highly correlated with performance. Specifically, the authors suggest desirable properties for the metric and show that current homophily measures do not satisfy some important properties, so suggest a new homophily measure to satisfy the... | Rebuttal 1:
Rebuttal: Thank you for the review and positive feedback! In our experiments we indeed mostly focus on synthetic datasets where connectivity patterns can be easily controlled and on semi-synthetic data previously used in the literature for the analysis of homophily measures. Originally, we have not experime... | Summary: This paper focuses on developing techniques to characterize graph node classification datasets. First, this paper has proposed a theoretical framework to develop measures of graph’s characteristics. Then two measures called “adjusted homophily” and “label informativeness” are proposed to easily characterize th... | Rebuttal 1:
Rebuttal: Thank you for the review and for acknowledging the usefulness of our theoretical framework! We address your questions and concerns below.
> Only Cora and Citeseer that be considered as homophilic graphs are used for showing the correlation between GNNs’ performance and LI, more heterophilic graph... | Summary: The paper proposes new measures to characterize homophily and heterophily in graph datasets. First, some desirable properties are asserted for a good measure of homophily. It is discussed whether various prior measures of homophily satisfy these properties, then a new measure called *adjusted homophily* is pro... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback and useful comments! We address your concerns below.
> While LI seems like a very sensible measure, it is rather straightforward, so I am left thinking: First, has it really not been proposed by some prior work, especially given that the general conc... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their comments and suggestions! In this general response, we address the comment raised by several reviewers about conducting additional experiments on real datasets.
Analyzing which measures better agree with GNN performance on real data is non-trivia... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Equivariant Adaptation of Large Pretrained Models | Accept (poster) | Summary: The authors tackle an important problem: equivariant NNs are very useful because of capturing important symmetries of the data that lead to higher sample efficiency and more accurate and robust predictions. Yet, EqNNs are very hard to scale because of their specialized architectures. In particular, they cannot... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment of our work and constructive feedback to improve the quality of the paper.
> More realistic settings without augmented test sets: I'd recommend standard out-of-distribution evaluation on IN-{adversarial, sketch, renditions}, INv2 etc. Another d... | Summary: The paper proposed a fine-tuning based adaptation method for equivariance in pre-trained models. The core of the method is the canonicalization function, which is trained to normalize the input to a common orientation. The paper investigates challenges associated with training canonicalization functions and pr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and suggestions.
> Experiments on equivariant tasks: The paper argues about the equivariant adaptation, but only invariance (and only to the rotation group) is demonstrated in the experiments.
We appreciate the reviewer's concern. To address this, ... | Summary: Paper proposes to enforce equivariance/invariance in pretrained models by using the recently proposed canonicalization functions that transform a given input to its canonical form. This allows one to enforce symmetry during the finetuning stage without the need for retraining the model. Paper uses a prior so t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their useful comments on the paper.
> Missing overall loss function equation: Details of the overall loss function is not presented or is assumed to be known to the reader [...]
We thank the reviewer for pointing out the absence of an overall loss function. The total l... | Summary: The work proposes a method for making large pre-trained models equivariant to specified group actions. The method uses the canonicalization approach, where the input is oriented to a specific orientation before being fed to the pre-trained model with the help of a trainable canonicalization network. The author... | Rebuttal 1:
Rebuttal: We thank the reviewer for their useful feedback. Below we address the reviewer's concerns.
> Concerns on experimental setup: [...] appropriate comparison should [...] perform data augmentation only with the discrete group actions while training the baseline.
We thank the reviewer for pointing o... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable and constructive feedback. We are pleased to see that they found our work simple (reviewers **45sc**, **qZrQ**), novel, and effective (reviewers **qZrQ**, **nPdb**) in addressing a relevant, important (**vNov**) and timely (**nPdb**) problem. In the... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
FITS: Modeling Time Series with 10k Parameters | Reject | Summary: The authors introduce FITS, a lightweight model for time series analysis. Unlike existing models that process raw time-domain data directly, FITS operates on the principle of manipulating time series through interpolation in the complex frequency domain. By discarding high-frequency components that have a negl... | Rebuttal 1:
Rebuttal: We sincerely apologize for the inconvenience caused by the color scheme of the figures. In the revised version, we will change the color scheme to enhance visibility and ensure a better viewing experience for readers.
- W2.1, Q2: Can the proposed framework be extended to handle multivariate time ... | Summary: This paper builds a model for time series learning in the frequency domain. The key idea is to discard high-frequency components to reduce the model size. This leads to a simple model under 10k parameters, 50x smaller than DLinear. Experimental results suggest the model can achieve comparable performance to th... | Rebuttal 1:
Rebuttal: Thanks for your insightful questions.
- W1&Q3: Why do we need such a small model, as DLinear may already be small enough for most of the edge devices?:
A: *We acknowledge that DLinear is already a compact model. However, for edge devices powered by **MCU (Microcontroller Units), e.g., smart se... | Summary: This paper proposed a novel method on time-series forecasting and anomaly detection. The solution relies on the frequency-domain feature of the given time series and proposes to use a linear model to interpolate the data in the frequency domain. After that, the model utilizes the inverse FFT operation to trans... | Rebuttal 1:
Rebuttal: - W1: The experiments are not promising. What's the number of the random runs and did you control the random seeds? I suggest to report the mean and std values of the results.
A: *We report the mean and std values across multiple runs in Section B.3 of the Appendix. Tab.4 shows that FITS is very ... | Summary: This paper proposes an impressive compact model, named FITS, for time series tasks, including forecasting and anomaly detection. FITS achieves manipulation to time series through interpolation in the frequency domain. The whole framework is quite simple and has remarkably few parameters. FITS achieves competit... | Rebuttal 1:
Rebuttal: Thanks for your positive evaluation of our work and the insightful questions.
- Q1, W1: It is appreciated to further list time consumption of the proposed model under different settings and compare time efficiency with baselines. Due to rFFT and irFFT in the model, time efficiency is the main co... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewers for their insightful feedback and recognition of our work's strengths:
1. The concept of conducting interpolation after projecting time series into the frequency domain is both **innovative and technically sound** [Reviewers PA7V, S88N, dtUP, pZTr].
2. The compr... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors present a transformer model for forecasting and anomaly detection. The model performs both forecasting and reconstruction in the frequency domain with a fraction of parameters compared to state-of-the-art transformers. The model demonstrates impressive performance on anomaly detection and forecasti... | Rebuttal 1:
Rebuttal:
- W1: Baselines for forecasting : The authors compare with only transformer-based models. I would encourage the authors to compare with advanced non-transformer-based models such as N-HiTS and N-BEATS.
A: *We show the comparison with mentioned N-HiTS and N-BEATS on MSE in the following table. F... | null | null | null | null | null | null |
Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networks | Accept (poster) | Summary: This work found that more data does not necessarily improve the pre-training of GNNs and then propose a method to adaptively select data for pre-training GNNs. Experimental results show the proposed method is effective.
Strengths: 1. this work is well motivated by the observation that more data does not neces... | Rebuttal 1:
Rebuttal: Dear Reviewer gzMz,
We really appreciate your valuable comments. We have followed closely the suggestions, and made clarifications and revisions accordingly. We hope the revised content could help to further strengthen our work.
>W1: The optimization problem in equation 5 is confusing, what is ... | Summary: The paper introduced an novel graph pre-training pipeline from a data-centric view by proposing a graph data selector. The proposed approach involved sequentially feeding representative data into the model for pre-training. These representative data were determined by a data selector, which utilized prediction... | Rebuttal 1:
Rebuttal: Dear Reviewer KR8J,
Thanks for your constructive suggestions to improve our manuscript. We have followed closely the suggestions, and made clarifications and revisions accordingly. We hope the revised and newly provided content could help to further strengthen our work.
>W1, Q1 and Q2: Predictiv... | Summary: This paper presents a data-centric framework, abbreviated as APT, for cross-domain graph pre-training. APT is composed of a graph selector and a graph pre-training model. The core idea is to select the most representative and informative data points based on the inherent properties of graphs, as well as predic... | Rebuttal 1:
Rebuttal: Dear Reviewer i2Gc,
We really appreciate your valuable comments. We have followed closely the suggestions, and made clarifications and revisions accordingly. We hope the revised and newly provided content could help to further strengthen our work.
>W1: This paper mentions both "data-active" and "... | Summary: This paper proposes a novel approach to pre-training graph neural networks (GNNs) called the data-active graph pre-training (APT) framework. This framework introduces a unique method that involves a graph selector and a pre-training model. These two components work together in a progressive and iterative way t... | Rebuttal 1:
Rebuttal: Dear Reviewer 16eq,
We really appreciate your insightful comments. We have followed closely the suggestions, and made clarifications and revisions accordingly. We hope the revised and newly provided content could help to further strengthen our work. Below is a point-by-point response.
>W1: predic... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning List-Level Domain-Invariant Representations for Ranking | Accept (spotlight) | Summary: The authors propose a new domain-invariant ranking method which enforces list-wise feature invariance as opposed to item-wise feature invariance. The authors prove a novel domain adaptation bound specifically for the ranking problem. Authors show strong empirical improvement over item-wise invariance methods.
... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time, comments, and support! We hope that the following addresses the concerns:
- **“the statement in line 141 [is] confusing”**
Thank you for the question—this was also a conceptual hurdle for us when we worked on this paper (please kindly find the new figure atta... | Summary: This paper focuses on domain adaptation in the context of ranking problems. The authors propose a novel approach called list-level alignment to learn domain-invariant representations for ranking tasks. They demonstrate the benefits of this approach through theoretical analysis and empirical experiments.
Exis... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time, comments, and support! We hope that the following addresses the concerns:
- **“the theoretical aspect may be considered weak… can be seen as an extension of existing domain adaptation theorems”**
The result established in [1] is in terms of accuracy for binar... | Summary: This paper proposes to learn domain-invariant representations for ranking. In contrast to prior works that typically consider item-level alignment, they introduce the concept of list-level alignment to learn higher-level invariant representations on the lists. The domain adaptation generalization bound is expl... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time, comments, and support! We hope that the following addresses the concerns:
- **“The proposed alignment relies on the traditional domain adversarial training, offering no significant advancements. What are the differences between the proposed alignment and prior ... | Summary: This paper proposes a novel approach for domain adaptation on list ranking problems. In significant contrast with previous works, they adopt aligning list-level representations directly instead of item-level representations, and argue for its relevance in improving on list-level metrics pertinent to the proble... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time, comments, and support! We hope that the following addresses the concerns:
- **“results on diverse ranking tasks… like fine-grained computer vision”**
Thank you for the suggestion, and it would be indeed interesting to see whether ListDA would be useful in (vi... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and the remarks!
- **Figure on item-level alignment vs. list-level alignment.**
We created a new figure (in the attached PDF) to better illustrate their difference as well as the point that the latter is a stronger requirement than the former. This figure w... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Efficient Hyper-parameter Optimization with Cubic Regularization | Accept (poster) | Summary: {Summary}
This paper proposes a stochastic cubic regularization type algorithm for hyperparameter optimization that does not depend on hyper-gradients. Theoretical analysis shows that the proposed method can converge to approximate second order stationary points with lower sample complexity than that of first-... | Rebuttal 1:
Rebuttal: Thank you for your comments on both the strength and weakness of our paper. For your questions raised in the weakness part, here are our answers to each of them:
> Q1: The algorithm design is a direct application of cubic regularization, and therefore is not novel.
A1: Our algorithm is different... | Summary: The authors propose a cubic regularization scheme for the outer loop optimization of bilevel optimization problems. The cubic regularization is very appealing since it can "avoid saddle points". Theoretically, authors extend the convergence results of the cubic regularization to inexact gradients to make it wo... | Rebuttal 1:
Rebuttal: Thank you for your comments that our idea is interesting. Here are our responses to the weakness and questions.
### Weakness
> W1: A lot of statements are not properly backed.
A1: The proposed method is backed by (i) theoretical analysis, which demonstrate that it can converge faster than exi... | Summary: This paper studies the problem of hyper-parameter optimization in the context of machine learning. Through stochastic relaxation, the problem can be formulated as bilevel optimization, and the authors propose to use cubic regularization to solve the optimization problem. It is shown that under some regularizat... | Rebuttal 1:
Rebuttal: Thank you for your comments for both the strength and weakness on our paper. We would like to first answer the questions raised in the weakness part:
> W1: While the authors provide theoretical guarantees for the proposed method, it seems that these guarantees can be directly derived from known ... | Summary: This paper proposes a new optimization based technique for hyper-parameter tunings using Adaptive Cubic Regularized Newton method (ARC) based on stochastic relaxation. The author highlights the limitations of the existing hyper-parameter optimization algorithms.They show that their suggested method achieves be... | Rebuttal 1:
Rebuttal: Thank you for your comments on both strength and weakness of our paper. Here are our responses to your comments on weakness and questions:
## Weakness
> W1: The major contribution of this work should be explicitly stated in a section called "Our contributions".
A1: Please see our response to Q1... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback. Here we colloct some common questions and reply them in this general response. A PDF file containing all revised figures is also uploaded.
> Q1: major contributions
A1: The major contributions of this work include:
(1) We propose to utili... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery | Accept (poster) | Summary: This paper introduces a novel perspective on categorization, framing it as an optimization problem with the goal of finding optimal solutions. The authors propose a self-supervised approach that can identify new categories during testing. They achieve this by assigning concise category codes to individual data... | Rebuttal 1:
Rebuttal: ***We thank the reviewer for finding our method and theory novel. We will use the provided references to enrich our paper both in related works and experimental analysis. Automatic hyperparameter tuning is an intriguing future work, we also provided the updated results vs. SoTA methods for stronge... | Summary: The paper conceptualize a category through the lens of optimization, viewing it as an optimal solution to a well-defined problem. The author propose a novel, efficient and self-supervised method capable of discovering previously unknown categories at test time.
Strengths: 1、The paper aims to tackle an importa... | Rebuttal 1:
Rebuttal: ***We thank the reviewer for finding the category definition an important problem, our framework novel, and our experimental design effective. We also agree that a theoretical foundation requires adequate empirical studies. Hence here we address the points raised by the reviewer to enrich our pape... | Summary: This paper questions a foundation problem that underpins the generalized class discovery (GCD): what defines a category. To this end, the authors proposed a self-learning-based method that solves GCD by modeling the implicit category hierarchy in the data. The proposed method allows control over category granu... | Rebuttal 1:
Rebuttal: ***We thank the reviewer for finding our proposed method novel and effective and our motivation decent. We also agree that a strong theoretical approach will be more applicable if it is accompanied by strong empirical results. Hence here, we provide the empirical results that the reviewer has ment... | Summary: This paper tackles the problem of generalized category discovery (GCD) by reframing the concept of a category with an implicit category code tree, which addresses three problems of conventional supervised learning, namely category hierarchies, label inconsistency, and open-world recognition. The contributions ... | Rebuttal 1:
Rebuttal: ***We thank the reviewer for finding our inspiration and theoretical derivation reasonable and adequate. We use the points raised by the reviewer for better clarification of our theory and implementation details. The binary tree visualization and discussing the fine-grained vs. generic dataset per... | Rebuttal 1:
Rebuttal: We thank all reviewers for their insightful feedback, and we appreciate that the reviewers found our paper theoretically and methodologically novel. There were a few shared questions about our paper, both in terms of comparing against the recently published state-of-the-art and visualizing our met... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models | Accept (poster) | Summary: This work presents a GAN-based TTS model, as an improvement on its predecessor StyleTTS. Notably, it includes a diffusion-based style encoder, which is one major difference compared to its predecessor. In addition, the proposed model directly produce waveform, in contrast to spectrogram in its predecessor.
... | Rebuttal 1:
Rebuttal: We are grateful for your positive remarks and appreciation of our work, and we appreciate your helpful feedback for clarification. Here are our responses to your insightful questions and concerns.
**1. Claim of Human-Level Performance on Single and Multispeaker Datasets:**
You are correct in poi... | Summary: In this paper, the paper proposes StyleTTS 2 for human-level text-to-speech, which is an end-to-end system with two-stage training (pretraining some modules and finetuning the whole network). Two main modules proposed in paper is style diffusion module to model the prosodic style information and WavLM adversar... | Rebuttal 1:
Rebuttal: We appreciate your positive assessment of our work and constructive feedback on our paper. Here are the responses to your questions.
1. Figure 1(b) represents the single speaker case only. In the multispeaker scenario, our model first encodes the speaker embedding (anchor style) and then samples ... | Summary: The paper presents Style TTS 2, a new TTS model with three main innovations 1) diffusion of a latent variable to capture style (everything in the speech signal that’s not enumerated by a phone sequence t and speaker variable c), 2) the use of a pretrained speech language model (SLM) as a discriminator and 3) a... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and insightful comments on our work. We appreciate your observations and feedback that have helped us identify areas for clarification and improvement, and here are our responses.
- **Ethical Concerns on Misuse**
We are grateful for your attention to the poten... | Summary: The authors of the paper have proposed a high-quality TTS model StyleTTS 2. Unlike previous StyleTTS work, StyleTTS 2 has a DDPM-based style generator, speech language model discriminators, and a differentiable aligner. All of these design decisions help improve
the overall performance of the model. As the aut... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our paper and your constructive questions. We appreciate your positive feedback on our work and your insightful comments. We would like to address your concerns and clarify certain aspects of our paper.
- **Concern about Complicated Training Procedu... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and effort in reviewing and improving our work. Here are some common concerns we would like to address:
***
**Source Code and Checkpoints**
We will make the trained checkpoints and source code for training and inference publicly available. We will add ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper introduces StyleTTS 2, a text-to-speech (TTS) model that leverages latent diffusion and adversarial training with SLMs. The most noteworthy thing is that the paper employs large pre-trained speech language models (SLMs), such as WavLM, as discriminators in the adversarial training process. This novel... | Rebuttal 1:
Rebuttal: We are grateful for your valuable time and effort in reviewing our paper. Your insights have helped us clarify certain points about our work. Here are our point-to-point responses.
- **Concern about Zero-Shot TTS and Prosodic Styles**
We acknowledge that this aspect may not have been clearly ar... | null | null | null | null | null | null |
DiffComplete: Diffusion-based Generative 3D Shape Completion | Accept (poster) | Summary: The paper tackles the shape completion task by introducing a diffusion-based technique. To do so, additional design choices have been made such as hierarchical feature aggregation and an occupancy-aware fusion strategy to better reproduce the shape and respect the details. Based on numerous quantitative and qu... | Rebuttal 1:
Rebuttal: Dear Reviewer 9z1Q,
Thank you for the valuable comments and encouraging feedback. We are happy to respond to each question below.
**Q1: The paper could use simpler, more understandable, and short sentences.**
A1: In the revision, we will carefully polish the manuscript to ensure better clarity ... | Summary: The authors present a diffusion-based neural network for 3D shape completion. They introduce a hierarchical and spatially consistent feature aggregation scheme to fuse partial and complete shapes, and an occupancy-aware fusion strategy to fuse multiple partial shapes. They demonstrate a new SOTA performance in... | Rebuttal 1:
Rebuttal: Dear Reviewer hw6P,
Thanks for the constructive comments. Please find our response to the specific points below.
**Q1: Explain more design ideas about the two feature aggregation schemes.**
A1: Our key design motivation is to enhance the completion accuracy and generalization ability. Then, we ... | Summary: The paper tackles the problem of probabilistic shape completion using diffusion models, learning from range scans. The main contribution of the paper comes from proposing 2 novel techniques: hierarchical feature aggregation for strong conditioning and occupancy-aware fusion technique. The method is tested on S... | Rebuttal 1:
Rebuttal: Dear Reviewer DbPU,
Thanks for the valuable feedback. We are happy to address each specific comment below.
**Q1: Explain more details about the differences from other diffusion-based models.**
A1: DiffComplete is specifically designed for high-quality shape completion. As a result, it differs f... | Summary: The paper introduces a diffusion-based approach, DiffComplete, to generate complete shapes conditioned on partial 3D range scans. The condition is represented as volumetric TSDF (truncated signed distance function), while the complete shape is represented as volumetric TUDF (truncated signed distance function)... | Rebuttal 1:
Rebuttal: Dear Reviewer KuxT,
Thanks for the encouraging feedback. We are grateful to see your recognition of our methodology, model performance, and manuscript writing. We address the specific comments below.
**Q1: Compare with the suggested baselines (especially point-cloud-based methods), e.g., PVD [1]... | Rebuttal 1:
Rebuttal: Dear all reviewers,
We sincerely thank your constructive comments and are grateful to see the recognition received:
- **Methodology:** Reviewer 9z1Q: "I like the adoption of ControlNet in the design"; Reviewer KuxT: "The ControlNet-style design is reasonable", "Fine-tuning the network for multip... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The proposed method aims to tackle the TSDF shape completion problem using diffusion models. That is, given one or several incomplete TSDFs obtained from partial scans of a single object produced by range sensors, the method generates a TSDF of complete shape while trying to preserve the geometric details of t... | Rebuttal 1:
Rebuttal: Dear Reviewer Ct9H,
In the following, we address all comments in the review. Our additional results validate that our method achieves a clear improvement over the newly-suggested baselines. We are happy to include these experiments in the final revision of the paper.
**Q1: Add comparisons with t... | null | null | null | null | null | null |
Spontaneous symmetry breaking in generative diffusion models | Accept (poster) | Summary: The paper explores the generative dynamic of diffusion models. It proposes spontaneous symmetry breaking and Gaussian late initialization scheme and they achieve better fidelity and diversity on the generated images.
Strengths: [S1] The paper supports the claims in both theoretical and empirical.
[S2] The pa... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' feedback and are happy to hear that our work supports our claims both theoretically and empirically. In the following, we will address the main issues raised by the reviewer.
### Point 1: Discussion of other generative models
Q: *“The other generative models such as V... | Summary: The authors approach the reverse diffusion process in score-based generative modeling from the perspective of spontaneous symmetry breaking, which may explain why generation is minimally affected by "late initializations" (e.g. initializing x_t at t=T, but simulating the reverse process starting at some t < T)... | Rebuttal 1:
Rebuttal: We are thankful to the reviewer for the comprehensive and insightful feedback. In what follows, we'll address the main concerns and questions.
### Point 1: Motivation
Q: *“The motivation of the work should be made more clear. If it is to improve generation speed, the model should be compared aga... | Summary: The authors investigate theoretically and empirical when (in terms of diffusion timestep) certain symmetries in a diffusion process are broken, corresponding to when "choices" are made about which qualitative features a generated data point should have. Their findings suggest that there is no symmetry breaking... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. Next, we will address the highlighted concerns and questions.
### Point 1: Gap between theory and experiments
Q: *“There is a fairly large gap between the simple example in Section 3.1, where we could reason analytically about when the symmetry is ... | Summary: The authors of this work propose a new method to accelerate the sampling of diffusion models. First, the authors define as fixed points of the reverse process, points where the drift function is 0. Then, the authors claim that if two paths of stable fixed points interact, then it is the noise that can make the... | Rebuttal 1:
Rebuttal: We wish to thank the reviewer for the detailed and insightful review. In what follows, we will address the main issues and questions.
### Point 1: Analytically tractable models
Q: *“The theoretical analysis has some simplifying assumptions that it is not clear to what extent they alter what happe... | Rebuttal 1:
Rebuttal: Dear reviewers,
We wish to thank you for the detailed analysis of our paper and for all the encouraging and constructive comments. We are extremely grateful for your work, as it will make our manuscript substantially better. Several important questions were raised, and we provided extensive answe... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors examine denoising diffusion models and their dynamics through the lense of spontaneous symmetry breaking. They characterize the dynamics of the denoising process into two stages: in the beginning, samples orbit a central fixed point and after a critical amount of time denoising of a ‘selected’ sam... | Rebuttal 1:
Rebuttal: We wish to thank the reviewer for the positive and insightful review. We are happy to hear that you found the one-dimensional example as insightful, as it certainly shaped our understanding of this fascinating phenomenon. To further solidify the intuition, in the updated manuscript we will also ad... | null | null | null | null | null | null |
CARE: Modeling Interacting Dynamics Under Temporal Environmental Variation | Accept (poster) | Summary: The paper aims to model out-of-distribution dynamics of environments with interacting entities using a seq-to-seq style graph neural net architecture. The proposed model attempts to capture the invariant aspects of the dynamical environment via a context embedding that follows a neural ordinary differential eq... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our paper and your insightful review. Here we address your comments in the following.
> Q1. The proposed method is novel per se. It is also intuitive and well-justified, but it appears to put together a number of existing tools in the mo... | Summary: The paper proposes a new model architecture for modeling multi-agent dynamics. The model consists of three parts (encoder, dynamics, decoder) where the encoder initializes the latent state of each agent and the context, the dynamics is modeled via NeuralODEs and the decoder is a standard MLP. New to the paper ... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our paper, your insightful comments and support. Your positive feedback is incredibly encouraging for us! In the following response, we would like to address your major concern and provide additional clarification.
> Q1: The paper is cal... | Summary: The main idea of this work is to propose a novel approach called Context-attended Graph ODE (CARE) for modeling interacting dynamical systems under temporal distribution shift. The paper formalizes the problem of temporal distribution shift in interacting dynamics modeling and proposes a probabilistic framewor... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our paper, your insightful comments and support. Your positive feedback is incredibly encouraging for us! In the following response, we would like to address your major concern and provide additional clarification.
> Q1: Where is Lemma 4... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We thank you for your careful reviews and constructive suggestions. We acknowledge the positive comments such as "Novel approach" (Reviewer ZaqZ), “Superior performance” (Reviewer ZaqZ), "Clarity" (Reviewer ZaqZ), "Extensive experiments” (Reviewer ZaqZ), “I like the idea and the e... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
H-InDex: Visual Reinforcement Learning with Hand-Informed Representations for Dexterous Manipulation | Accept (poster) | Summary: The paper works on the problem of visual RL dexterous manipulation with pre-trained representations. Specifically, the authors propose to fix the pre-trained Convolution layers and adapt only the BatchNorm layers on a small amount of in-domain data, and then learn the policy based on the pre-trained feature wi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments and suggestions. We address each of your comments in the following.
**Q1:** For some tasks, the proposed method seems on par or slightly worse than baselines (e.g., Door, Relocate Mug, Relocate Mustard Bottle);
**A1:** In the field of deep rein... | Summary: The paper proposes H-InDex for dexterous manipulation tasks with a 30 DoF robotic hand in simulation. The method consists of three stages: the first stage adopts a pre-trained network that is trained with a 3D human hand pose estimation dataset; the second stage adapts the representation with self-supervised k... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments and suggestions. We address each of your comments in the following. We omit the questions due to space limits.
**Q1:** L.145-146 .....
**A1:** We have conducted a more comprehensive evaluation on our Stage 2 and the results are given in Figure ... | Summary: This paper studies a three stage framework for adapting pre-trained visual representations for dexterous manipulation tasks. Specifically, this work proposes only adapting a subset of network parameters in stages 2 and 3. The approach is tested on 12 manipulation tasks from Adroit and DexMV. These experiments ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments and suggestions. We address each of your comments in the following.
**Q1:** Large parts of the proposed approach are directly applied from prior work including the pre-trained visual representation (stage 1) and the learning objective from stage... | Summary: This paper introduces a framework called H-InDex that uses human hand-inspired visual representation learning to solve complex manipulation tasks with reinforcement learning. The framework consists of three stages and outperforms other methods in challenging dexterous manipulation tasks.
Strengths: * A novel ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments and suggestions. We address each of your comments in the following.
**Q1:** The method seems only to be able to perform on a single object on each task, while the vision model should be able to generalize on category-level objects.
**A1:** Achi... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments. We have addressed all your individual comments. We wanted to thank the reviewer for acknowledging the novelty and the empirical evaluation of our work – “a novel and effective method” (GX9c), “experiments are extensively conducted” (rofX), ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
K-Nearest-Neighbor Local Sampling Based Conditional Independence Testing | Accept (poster) | Summary: This paper argues that conditional independence (CI) testing becomes challenging because of high-dimensional conditioning variables and limited data samples. To address these issues, the authors propose a testing approach incorporating a class-based conditional mutual information (CMI) and a $k$-nearest-neighb... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful review!
**Weakness 1**. The binning strategy can only handle conditioning variables $Z$ with very few dimensions. Please see the response for Question 1. We therefore did not consider the comparison with the methods employing a binning strategy in the paper... | Summary: In this paper, the authors present a method for conditional independence (CI) testing based on a conditional mutual information (CMI) estimator.
This CMI estimator is based on a classifier over 1-NN samples. From the CMI estimates, the authors present a hypothesis testing procedure for CI based on multiple it... | Rebuttal 1:
Rebuttal: We thank the reviewer for the important review!
**Weakness 1**. To show which parts of our approach contribute to the significant results, we have done some additional experiments as per your suggestions. Specifically, the classifiers we evaluate include DNNs, XGBoost, and Random forest. The sim... | Summary: In this paper, the authors propose a novel CMI estimator with a classifier-based approach. The estimated CMI is then used for conditional independence testing. This approach achieves asymptotic type I & II control and does not require prior assumptions on the distribution, types of correlations, or knowledge o... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful review!
**Weakness 1**. The Maximum Likelihood Mutual Information (MLMI) method proposed by Suzuki et al. (2008) directly models the density ratio and avoids density estimation. MLMI estimates the density ratio using maximum likelihood and formulates it as... | Summary: The paper proposes a novel method to test conditional independence (CI) by estimating conditional mutual information (CMI). A kNN local sampling strategy is employed to empirically sample from an unknown conditional distribution. The method is proved to have type-I error controlled, and power converging to 1.
... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful review!
**Weakness**: Please refer to the global response for details regarding the claim “ high-dimensional conditioning variables". Further, we conduct additional experiments to assess the performance of our tests in higher-dimensional scenarios. We genera... | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed and considerate feedback. We offer the following clarifications. **A PDF file including 5 tables is attached.**
1. About the claim "**our method avoids sample splitting**", we emphasize that
our proposed method **allows the entire dataset to be used ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
SPA: A Graph Spectral Alignment Perspective for Domain Adaptation | Accept (poster) | Summary: The authors present a new graph Spectral Alignment (SPA) framework for domain adaptation. The method consists of two main components: (i) a coarse graph alignment mechanism that uses a novel spectral regularizer to align domain graphs, and (ii) a fine-grained message propagation module that employs a neighbor-... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback, and we would like to address your concerns in detail below:
> **[Weakness 1]** "[...] Neighbor-aware Propagation is abbreviated as "L_nas" in the paper. However, I think that "L_nap" would be a more appropriate abbreviation."
**[Answer]** Thanks for your ... | Summary: The paper proposes a domain adaptation method called Spectral Alignment (SPA). The proposed SPA utilizes spectral distance defined as the difference between eigenvalues of the Laplacian matrix to quantify the distribution gap. Then SPA employs this distance as regularizer for domain alignment. Additionally, SP... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback, and we would like to address your concerns in detail below:
> **[Weakness 1 + Question 1]** "The contribution of the paper is not clear. First, the paper directly employs a spectral distance from [1,20,59] without technical development to measure and narro... | Summary: The paper proposed a graph SPectral Alignment (SPA) framework which uses a coarse graph spectral alignment and neighbor-aware self-training mechanism. The authors compared the proposed method with different UDA methods using several benchmark datasets.
Strengths: The paper is generally well-written and clear.... | Rebuttal 1:
Rebuttal: Thanks sincerely for the constructive reviews, and we have made great efforts to address all these concerns.
> **[Weakness 1]** "Please add more details that how to construct a dynamic graph, especially for weighted edge and metric function."
**[Answer]** Thanks for your suggestion. We put diffe... | Summary: This paper proposes a novel graph spectral alignment framework for unsupervised domain adaptation, jointly balancing the inter-domain transferability and the intra-domain discriminability. First, they cast the domain adaptation problem to graph primitives by composing a coarse graph alignment and aligning doma... | Rebuttal 1:
Rebuttal: Thank you for these thoughtful comments, and we would like to address your concerns in detail below:
> **[Weakness 1 + Question 1]** "To support the claim of balancing transferability and discriminability, the authors offer the figures of visualized features. More ablation studies to express the b... | Rebuttal 1:
Rebuttal: We thank the reviews for their positive views about different aspects of our work which say that:
* our proposed method is: "an attractive method which works well and inspires follow-up research" (Reviewer AnYf), "different from the previous domain adaptation methods" (Reviewer Yk67), "highly inno... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work proposed a graph spectral alignment model for unsupervised domain adaptation. They evaluated on several benchmarks and show good performance, especially in DomainNet and Office-Home, compared with existing UDA methods. Generally, the paper is easy to follow.
Strengths: The paper is generally easy t... | Rebuttal 1:
Rebuttal: Thank you for your valuable time and review, and we have made great efforts to address all these concerns below:
> **[Weakness 1]** "The novelty of this work is limited. The graph alignment is simply extended from BSP [12] and Graph Matching like SIGMA [39]. The Neighbor-aware Self-training Mechan... | null | null | null | null | null | null |
ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion | Accept (poster) | Summary: The paper introduces ProtoDiff, a novel addition to the family of Prototypical Network methods that uses a diffusion model to obtain better class prototypes at inference time.
Prototypical Networks perform classification with metric-based comparisons between examples and “class prototypes”, variables computed... | Rebuttal 1:
Rebuttal: ***Thank you for your time, effort, and constructive comments.***
* **Weakness**
Answer: Thank you for the thoughtful feedback and the reference to the work by Nava et al. We acknowledge the concern that the experimental gains might seem marginal and that the focus appears on incremental improv... | Summary: This paper proposes using the diffusion model to learn a prototypical network. During the meta-train stage, an overfitted prototype (by fine-tuning the classifier until it gets a probability of 1 for the task) is obtained first, then given a vanilla prototype (by simply averaging the features) as input, the di... | Rebuttal 1:
Rebuttal: ***Thank you for your positive feedback and for recommending acceptance of our paper.***
* **Lower and not diverse available tasks**
Answer: We acknowledge the reviewer's point regarding the data-intensive nature of diffusion models. Indeed, performance can be impacted when faced with a limited ... | Summary: This paper proposes a prototype-based meta-learning method called ProtoDiff. During meta-learning stage, ProtoDiff introduces a task-guided diffusion process, learning a generative process conditioned on vanilla prototype for more robust prototype representation. ProtoDiff is extensively validated on within-do... | Rebuttal 1:
Rebuttal: ***Thank you for your time, effort, and constructive comments.***
(1) **Technical details are missing**
Answer: Our ProtoDiff model is constructed using a design inspired by GPT-2. This includes a 12-layer transformer, a 512-dimensional linear transformation, an attention mechanism with 16 head... | Summary: This paper tries to improve Prototypical Network in few-shot learning. Specifically, the authors use a diffusion model to predict optimal prototypes from initial prototypes. In each iteration of the meta-training phase, the optimal prototypes are obtained by several inner loops on the current task, then a diff... | Rebuttal 1:
Rebuttal: ***Thank you for your time, effort, and constructive comments.***
* **Motivation of ProtoDiff**
Answer: We acknowledge your insight regarding using a stochastic or diffusion model. Our choice to employ a generative model like the diffusion process extends beyond simply predicting optimal prototy... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting | Accept (poster) | Summary: This research focuses on distributed learning problems and introduces a novel approach that incorporates three key elements: variance reduction, compression, and partial participation. It extends the DASHA method to address the partial participation scenario in both finite-sum and stochastic scenarios. In the ... | Rebuttal 1:
Rebuttal: Thank you for the review!
> The presentation can be further improved in several ways to enhance clarity and readability. ...
We agree. One can see that we separate assumptions in the parts "1. Gradient Setting.," "2. Finite-Sum Setting," and "3. Stochastic Setting," which begin at Lines 33, 38, ... | Summary: This paper introduces a novel method for distributed optimization and federated learning that integrates three critical components: variance reduction of stochastic gradients, partial participation, and compressed communication. The proposed method is shown to have optimal oracle complexity and leading-edge co... | Rebuttal 1:
Rebuttal: > Despite the significant theoretical contributions, this paper falls short in one crucial aspect, which restricts its overall impact and practical applicability.
> Lack of Empirical Validation: The major limitation of this paper is the absence of empirical experiments to substantiate the theoret... | Summary: This manuscript considers distributed non-convex optimization in the federated learning setting. It proposes an algorithm DASHA-PP that brings three important features of federated learning together: i) variance reduction, ii) compressed communication, and iii) partial participation. The authors derive the ora... | Rebuttal 1:
Rebuttal: Thank you for the comments!
> Besides the new update rule, it seems to me that the mathematical derivations in the manuscript are very similar to those in [1]. In that sense, I find the contribution of the manuscript is rather incremental.
In Lines 168-182, we describe the difference between the... | Summary: The paper injects existing algorithm for distributed optimisation in the compressed and variance reduced setting with a mechanism for partial participation of nodes. Theoretical guarantees are given in the general non-convex setting, but also under PL-condition. The theoretical results are verified empirically... | Rebuttal 1:
Rebuttal: Thank you for the review!
> The paper is very technical (if one wishes to follow proofs of the claims) and builds to an extensive amount of previous work, thus it is difficult to be appreciated by readers that do not belong to this specific research community. The appendix is about 90 (!) pages a... | Rebuttal 1:
Rebuttal: Dear AC and Reviewers,
We are now presenting new **extra experiments**. See the attached pdf to this rebuttal. If you do not see the pdf here, click "Revisions," after "Author Rebuttal by Authors" and there you will find a link to the pdf
In these experiments, we compare our new method with the ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Robust Learning for Smoothed Online Convex Optimization with Feedback Delay | Accept (poster) | Summary: This paper studies Smoothed Online Convex Optimization (SOCO) problems where the target is to minimize the sum of a per-round hitting cost and a switching cost that penalizes temporal changes in actions.
The most significant difference from previous studies is that they assume a delayed hitting cost and a non... | Rebuttal 1:
Rebuttal: We appreciate your efforts and time in reviewing our paper.
`Average cost bound:` The reviewer is correct that an average cost bound can be established based on the worst cost bound. We provide Theorem 4.2 to confirm that it’s beneficial to train the ML model in a robustification-aware manner ac... | Summary: This paper considers the SOCO problem with multi-step nonlinear switching costs and feedback delay. The authors propose the RCL algorithm, which projects the ML predictions on to a robust action set determined by the expert’s predictions. They show that RCL maintain the $(1+\lambda)$-competitiveness against th... | Rebuttal 1:
Rebuttal: We appreciate your efforts and time in reviewing our paper.
`Explanation of costs difference bounds:` The smoothness of the cost functions implies that, given a bounded difference in two actions, their cost difference is also bounded (formally stated shown in Lemma C.1 in the appendix). Thus, wh... | Summary: This paper considers the smoothed online convex optimization problem, in which, in addition to the standard hitting cost incurred per step, the algorithm also incurs a switching cost which relates to changes in its chosen action. The paper considers switching costs which are multi-step (relate to the previous ... | Rebuttal 1:
Rebuttal: We appreciate your efforts and time in reviewing our paper.
`Significance:` We’ll revise the paper to improve its presentation. Our contribution is a novel ML-augmented algorithm that exploits ML predictions to improve the average performance while bounding the worst-case performance in a genera... | Summary: This paper studies the Smoothed Online Convex Optimization with multi-step nonlinear switching costs and feedback delay. In this setting, they propose Robustness-Constrained Learning (RCL), which combines existing online algorithm with a novel reservation cost to robustify untrusted ML predictions. Theoret... | Rebuttal 1:
Rebuttal: We appreciate your efforts and time in reviewing our paper.
`Reservation cost and complexity:` If we choose an action different from the expert without a reservation cost, it’s possible that the competitiveness against the expert is violated (an example is provided in Lines 210-217). Thus, for g... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies a problem, namely, Smoothed Online Convex Optimization (SOCO) with feedback delay and multi-step nonlinear memory, proposing a machine learning augmented online algorithm (RCL) with certain theoretical guarantees. This algorithm considers to implicitly hedge the decisions made by online and ... | Rebuttal 1:
Rebuttal: We appreciate your efforts and time in reviewing our paper.
`Sufficient condition:` The prior study [1] proves an "impossibility" result: 1-consistency and finite robustness are not achievable simultaneously without further assumptions due to the fundamental hardness of the SOCO problem (even wit... | Summary: This paper studies Smoothed Online Convex Optimization with multi-step nonlinear switching costs and feedback delay. They propose an ML-augmented online algorithm named RCL that combines ML predictions with an expert online algorithm. The authors show that RCL can guarantee $(1 + \lambda)$ competitive ratio ag... | Rebuttal 1:
Rebuttal: We appreciate your efforts and time in reviewing our paper.
`Algorithm design and novelty:` It’s essential to construct a robust action set to hedge against future uncertainties for guaranteed robustness in learning-augmented online algorithms. Nonetheless, how to achieve so is very challenging.... | null | null | null | null |
Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms | Accept (poster) | Summary: The paper considers the problem of Quadratic Hessian bandit and establishes both lower and upper bounds on algorithm performance to provide a characterization of simple regret in this setting. The authors also propose an algorithm that is agnostic to the knowledge of the Hessian.
Strengths: The paper consider... | Rebuttal 1:
Rebuttal: Thank you for the suggestions and below are the detailed responses.
Q1: Details for the Proof of Theorem 2.1.
A1: We will provide additional details for the intermediate steps, including additing a reference for the optimality of MMSE estimators. We will also correct the typo in equation (7).
Q... | Summary: This papers studies quadratic stochastic zeroth-order optimization and provide lower and upper bounds on the minimax simple regret. First, the authors provide an asymptotically tight upper bound of $\frac{1}{2}({\rm Tr}(A^{-1/2}))^2$ where $A$ is the Hessian of the quadratic. Then, the authors provide asymptot... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments, and will revise the identified typos. Below are the point-to-point responses to the comments.
Q1: Energy allocation vs energy spectrum
A1: Energy spectrum refers to the particular function defined above line 86, and we used energy allocatio... | Summary: The paper considers the black-box optimization problem of quadratic functions specified with PSD matrices. The paper shows matching upper and lower sample complexity bounds depending on the matrices. For the upper bound, the paper proposes algorithms.
Strengths: The paper gives tight upper and lower sample co... | Rebuttal 1:
Rebuttal: Thank you for the feedback. Convex quadratic functions depict the local geometry of strongly convex functions near the critical point, and their sample complexity remains an open question in the online learning community. Although our result does not directly apply to general convex functions. Ful... | Summary: This paper studies zero-th order quadratic optimization with bandit feedback. It provides a comprehensive analysis of the optimal sample complexity, which depends on the Hessian of the objective function. There are two contributions. Firstly, it establishes lower bounds on Hessian-dependent complexities using ... | Rebuttal 1:
Rebuttal: Thank you for the comments. We have now provided the following point-to-point response.
Q1: The definition of dim$A$?
A1: In the submitted version, our result is stated for dim$A=d$ where dim refers to the dimension of the Euclidian space over which A is defined. While our result holds true unde... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Flexible Attention-Based Multi-Policy Fusion for Efficient Deep Reinforcement Learning | Accept (poster) | Summary: This paper introduces KIAN, a method for leveraging multiple sub-optimal policies to solve reinforcement learning tasks. The paper starts out by introducing a new Knowledge-Grounded MDP, which adds a set of external knowledge policies to the traditional MDP framework.
To leverage these external knowledge pol... | Rebuttal 1:
Rebuttal: 1. *"In such a case, whenever the knowledge set is updated by adding or replacing policies, prior methods require relearning the entire multi-policy fusion process, even if the current task is similar to the previous one. This is because their designs of knowledge representations are intertwined w... | Summary: The authors of this work introduce Knowledge-Grounded RL (KGRL), an RL framework that combines multiple knowledge policies to achieve human-like efficiency and flexibility in learning. They propose a new actor architecture called Knowledge-Inclusive Attention Network (KIAN) that enables arbitrary combinations ... | Rebuttal 1:
Rebuttal: 1. *The use of only 3 or 5 seeds in the experiments may be insufficient, especially when considering the large error bars observed in some models. The authors should consider increasing the number of seeds to improve the statistical robustness of the results.*
Thank you for the suggestion! We hav... | Summary: Humans can learn by aggregating external knowledge from others’ policies of attempting a task. While prior studies in RL have incorporated external knowledge policies for sample efficiency, there still remains the generalization problem to be solved that agents have difficulties to perform arbitrary combinati... | Rebuttal 1:
Rebuttal: 1. *Generally this work has more related works than in the related work section, such as multi-task imitation learning etc,.*
We understand that other works not listed in the related work section may seem to be addressing a similar problem as KGRL. However, we would like to point out that their c... | Summary: This paper defines the Knowledge-Grounded RL setting, a general RL setting for integrating knowledge (in the form of policies) into a policy to learn new tasks efficiently. Essentially this setting is similar to the MDP setting except that the agent is also given a set of knowledge policies to utilize. The pap... | Rebuttal 1:
Rebuttal: 1. *I am concerned about statistical significance. In particular, only 5 seeds are run, and the performance benefit in many cases is minimal, which may quite possibly be attributed to randomness.*
Thank you for pointing this out! We have increased the number of random seeds run for all experiment... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful feedback! We address the shared reviewers’ comments below and will incorporate all feedback in the final paper.
### Experimental Significance
(Reviewer 7i4w and CuzY)
We have increased the number of random seeds to 10 for each experiment within the giv... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A General Framework for Robust G-Invariance in G-Equivariant Networks | Accept (poster) | Summary: The paper introduces a novel method to enhance robustness and group-invariance in group-equivariant convolutional neural networks (G-CNNs), named the G-triple correlation (G-TC) layer. This approach uses the concept of triple correlation on groups, a polynomial invariant map that is also complete, unlike many ... | Rebuttal 1:
Rebuttal: Dear Reviewer aA5W,
We thank you for the thoughtful review. We respond to your comments and questions below:
**Discrete Groups**
We thank the reviewer for mentioning the very interesting question of how to generalize our method to continuous groups. We first note that many of the papers that ma... | Summary: This paper proposes a $G$-triple-correlation ($G$-TC) pooling layer in order to achieve group-invariance in group-equivariant convlutional neural networks (G-CNNs). Compared to the widely used $G$-pooling layers, the proposed $G$-TC layer is supposed to be "complete" in the sense that it removes only the varia... | Rebuttal 1:
Rebuttal: Dear Reviewer Axj4,
Thank you for the time you’ve taken to read and review our paper. We address your comments and questions below:
**Generalization to Steerable G-CNNs**
Please see the global response on steerable G-CNNs.
**Computational Complexity**
Please see the global response on compu... | Summary: This paper introduces a concept called G-triple-correlation (G-TC) layers, which are used in combination with G-equivariant convolutional layers to achieve G-invariant mapping functions. In contrast to conventional methods such as max or average G-poolings, which result in information loss, the proposed G-TC l... | Rebuttal 1:
Rebuttal: Dear Reviewer NbDh,
We thank you for the thoughtful review, and address your comments and questions below:
**Computational Complexity**. Please see the global response on computational complexity.
**ReLU & Nonlinearity**. For the G-TC model, we remove the ReLU that is standardly performed afte... | Summary: This work proposes to use the triple correlation to achive the group invariance. Unlike max pooling, the triple correlation preserves the entire information from the original signal, which may improve the quality of solutions for downstream tasks. Experiments show effectiveness of the proposed method.
Strengt... | Rebuttal 1:
Rebuttal: Dear Reviewer E2w2,
Thank you for your review. We appreciate your attention and address your comments and questions below:
**Novelty.** Although the triple-correlation in its translation-invariant form has a long history in signal processing, it is relatively unknown in deep learning. Google sc... | Rebuttal 1:
Rebuttal: We thank the four reviewers for their time and attention. We have provided responses to each reviewer for unique points made in the review. Here, we highlight a few points that appear across reviews, to avoid redundancy.
**Generalization to Steerable G-CNNs**
We thank reviewer Axj4 for bringing... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Single-Pass Pivot Algorithm for Correlation Clustering. Keep it simple! | Accept (poster) | Summary: The paper shows a single-pass semi streaming algorithm for correlation clustering (minimizing disagreements) which computes a $3+\epsilon$ approximate solution using $O(n/\epsilon)$ words. This should be compared with:
* a 5-approximate algorithm using O(n) words
* a $3+\epsilon$ approximate algorithm using... | null | Summary: The paper studies the correlation clustering problem in the semi-streaming model obtaining a $(3+\varepsilon)$-approximation to the problem with $O(\frac{n}{\varepsilon})$ words of memory. This is an improvement over [CKLPU'22] who gave the same approximation with $O(n\log n)$ words of memory (I'm not sure of ... | Rebuttal 1:
Rebuttal: 1. The algorithm by [CKLPU'22] uses $n \log(n)/\epsilon$ words of memory.
1. The memory usage is O(kn). It is linear in $n$ if $k$ is fixed.
2. __l131: I think the runtime bound can be improved to ...__
Thank you! You are right.
3. That's correct.
4. You are right.
5. __When you say the opti... | Summary: This paper demonstrates that the celebrated Pivot algorithm for (unweighted, complete) correlation clustering (with the min disagreements objective) can be adapted to the single-pass, semi-streaming setting, to yield a (3+\eps)-approximation in expectation using O(n/eps) words of memory. (Note that the origina... | Rebuttal 1:
Rebuttal: Thank you for your suggestions! We will revise the introduction and include more background on the prior work. We will also use a different name for "singletons". Finally, we will fix the _typos/omissions_.
---
Rebuttal Comment 1.1:
Comment: I thank the authors for their responses. While it is t... | Summary: This paper provides a streaming algorithm for correlation clustering on complete graphs that uses O(n/eps) space and has an approximation factor of 3+eps. The algorithm is based on the combinatorial pivot algorithm.
The prior results in the streaming setting give 5 approx in the better space of O(n); and ano... | Rebuttal 1:
Rebuttal: You are right $U_1 = V$. Thank you! | Rebuttal 1:
Rebuttal: We thank the anonymous reviewers for their valuable comments. We are grateful to them for carefully reading our paper, giving us helpful suggestions, and providing a list of typos. We will address all the feedback we received in the final version of this paper and, of course, fix the typos and add... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper considers the problem of correlation clustering on a complete graph, where all the edges are assigned either "+" or "-". The goal is to come up with a clustering that maximizes agreement with the edge labels. This is a well studied problems in approximation algorithms. There is an elegant 3 approxim... | Rebuttal 1:
Rebuttal: __When you say that the stream only consists of positive edges, it is worth pointing out that you are working on the complete graph and the remainder of the edges are assumed to be negative.__
Thank you! That's correct. We will clarify this point in the final version of this paper. | null | null | null | null | null | null |
Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning | Accept (poster) | Summary: This paper thoroughly studies a particular undesired aspect of state-of-the-art self-supervised learning models: deja-vu memorization, where the models end up memorizing specific parts of the image instead of learning just a semantically meaningful association. The authors provide extensive empirical results t... | Rebuttal 1:
Rebuttal: Thank you for reading our work, and for your suggestions/questions! We have attempted to answer your primary questions and concerns below. Please let us know if you have any further questions, or if anything is unclear.
**Authors do not seem to talk about the impact of different augmentations on... | Summary: This paper shows how self-supervised learning (SSL) models can memorize information about their training data in ways that are not intended. SSL is a technique for learning representations of unlabeled data by training a model to solve a "pretext" task; for example, we might take training images A, B, and C an... | Rebuttal 1:
Rebuttal: We sincerely appreciate the related work references!
We indeed can answer your questions pertaining to the dataset splits. We will edit the paper to make these points more precise. Please do let us know if we can provide any further clarifications.
**Section 3.1 says the datasets A, B, and B ar... | Summary: Self-Supervised Learning algorithms provide a label-free alternative for representation learning and is widely used in vision, and language. In this work, the authors try to understand the information content of representations learned by SSL algorithms through the lens of memorization. In particular, they def... | Rebuttal 1:
Rebuttal: Thank you for giving our paper a detailed read, as is clear from you thorough summary! We are happy to hear the strengths you saw in our work, and try to address some of your primary questions below. Please let us know if you have any follow up questions.
**Explainability: [...] Although the aut... | Summary: This work investigates to what degree self-supervised models exhibit memorization of particular samples in the training set. The authors demonstrate that a simple crop of an image, only comprised of the background, is enough for an adversary to learn the associated class label of the foreground object. Moreove... | Rebuttal 1:
Rebuttal: We are glad to hear that you found our work timely, and our experiments interesting. Please let us know if our responses below answer your primary questions.
**I struggle to really see the privacy concern here. How likely is it really for someone to have access to only a crop of an image, while ... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their thoughtful remarks on our work. Reviewer H9Sq raised an important clarification, asking whether sets $A$ and $B$ are disjoint. We want to clarify for everyone that indeed our set $A$ and $B$ are always disjoint (which is fundamental in our analysis). ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression | Accept (poster) | Summary: The authors address the problem of kernel ridge regression with a finite rank kernel, in the under-paramatrized regime. They prove sharper upper bounds on the test error, compared to previous existing works.
Strengths: The discussion is very well-written and easy to follow, with a number of illustrations bein... | Rebuttal 1:
Rebuttal: ## Experimental details
**\[review\]** *” A minor remark is that while all experimental details are provided in Appendix D, it would be good to add at least a short description of the target functions and regularization used in Fig.1 and 2 in the main text or in the corresponding captions.”*
**\... | Summary: The paper highlights the inadequacy of existing statistical learning guarantees for general kernel regressors when applied to finite-rank kernels. The authors have addressed this issue by developing non-asymptotic upper and lower bounds for the test error of finite-rank kernel ridge regression. These new bound... | Rebuttal 1:
Rebuttal: ## Lower bound
**\[review\]** *”Low bound is a main argument of this paper, but all details are given in the Appendix.”*
**\[answer\]** Yes, we should have stated the lower bound in the main theorem explicitly. As mentioned in responses to other reviewers, it is in hindsight very clear to us tha... | Summary: This paper analyzes the test error of ridge regression with a finite rank kernel. The finite rank kernel appears in several practical settings such as transfer learning and random neural networks. The authors provide a new analysis in this setting using tools from random matrix theory. A detailed comparison to... | Rebuttal 1:
Rebuttal: ## Technical details and novelty
**\[review\]** *”The technical details of the proof are not given and the novelty of the analysis is not explained in detail.”*
**\[answer\]** We will refactor the revised paper so that these things are stated more clearly and more prominently, while relegating s... | Summary: The paper studies the kernel ridge regression under the non-asymptotic setting. The authors give the upper and lower bounds for bias and variance term, respectively. The authors argue the results improve upon those in Bach 2023.
Strengths: The paper is well-structured. The authors give rigorous proofs, follow... | Rebuttal 1:
Rebuttal: ## Polishing
**\[review\]** *"The paper requires further improvement and polishing in writing."*
**\[answer\]** Thank you for pointing out certain issues with the writing; we will address them in the revised edition.
## More references
**\[review\]** *"As mentioned in Bach 2023, more refined b... | Rebuttal 1:
Rebuttal: **General response to all the reviewers:** Thank you for your comprehensive review of our paper. We sincerely appreciate the time and effort you have dedicated to evaluating our work.
Below, we outline the primary issues highlighted by the reviewers based on our interpretation of their feedback. ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper gives a refined analysis of the test error of kernel ridge regression with a finite kernel in the underparametrized regime, where the rank M of the kernel is smaller than the number of data points N. The analysis improves upon previous ones by providing tighter non-asymptotic upper and lower bounds,... | Rebuttal 1:
Rebuttal: ## Explicit lower bound
**\[review\]** *“I would also suggest that the authors include the explicit lower bounds in the main paper. Given that this a major focus of the paper it should not be required for the reader to go to the appendix.”*
**\[answer\]** We concur with the reviewer's viewpoint... | null | null | null | null | null | null |
Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction | Accept (poster) | Summary: TSAD techniques are targeted towards detecting either point or contextual anomalies, but often struggle to adequately capture both simultaneously. In this work, the authors propose a novel reconstruction-based AD technique that introduces the notion of a nominality score and a subsequent induced anomaly score.... | Rebuttal 1:
Rebuttal: We extend our sincere gratitude to Reviewer *UPqe* for their detailed review and valuable insights. We will address the raised points in the revised version accordingly.
#### 1. More discussion or synthetic examples that demonstrate whether there are certain properties of the monitored data that ... | Summary: This paper proposes an unsupervised time series anomaly detection algorithm called NPSR (Nominality score conditioned TSAD by Point/Sequential Reconstruction), which combines both point-based and sequence-based reconstruction models. Specifically, it proposes a nominality score which is the ratio of a contextu... | Rebuttal 1:
Rebuttal: We express our sincere appreciation to Reviewer *gJSP* for their thorough review and valuable insights. The points raised will be duly addressed in the revised version.
#### 1. From experiments it seems the most important part is the point-based reconstruction. In other words, the rest of the pro... | Summary: This paper proposed a novel idea to handle the time-series anomaly detection problem by calculating the "nominality score"(concerns on this name are in the question part and I'll keep using this name in the following review) and the induced anomaly score. And the F1 score can be mathematically proved to improv... | Rebuttal 1:
Rebuttal: We express our heartfelt gratitude to Reviewer *EBhj* for conducting a comprehensive review and providing valuable insights. We will take into account the raised points and incorporate them appropriately in the revised version.
#### 1. The analysis of a bound or expectation of improvement for the... | Summary: Basically, this paper aims to consider anomalousness of data points both from a point perspective, which is independent of the temporal relationships in the data, and contextual perspective, which reflects temporal relationships in the data. The paper derives an induced anomaly score that considers both.
Stre... | Rebuttal 1:
Rebuttal: We extend our sincere appreciation to Reviewer *m3iW* for conducting a thorough review and offering valuable insights. The raised points will be thoughtfully considered and integrated into the revised version.
#### 1. The datasets on which testing is performed have rather high anomaly rates. The ... | Rebuttal 1:
Rebuttal: We sincerely express our gratitude to all the reviewers for their thorough reviews and valuable insights.
A significant hurdle in time series anomaly detection involves effectively modeling time-dependent relationships and detect both contextual and point anomalies accurately. To tackle this issu... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection | Accept (oral) | Summary: 1. The authors prove that transformer models can perform several machine learning tasks, such as least squares, ridge regression, lasso and learning of GLMs.
2. The authors prove that transformer models can select an algorithm from a pool of possible ones, in two scenarios, post ICL validation and pre ICL test... | Rebuttal 1:
Rebuttal: We thank the reviewer for the strong positive feedback on our paper! | Summary: They take an in-depth empirical and also theoretical analysis of the in-context learning abilities of transformers on various tasks. Their theoretical analysis of the learned algorithms is especially strong. The work in my eyes presents an extension of previous works in this field to more complex ICL problems ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback on our paper. We respond to the specific questions as follows.
> …Authors motivate this with an example where the hyperparamter in a linear regression varies. I would expect the transformer to be able to learn to generalize these parameters, given a... | Summary: This paper theoretically investigate the in-context learning ability from the approximation view. The authors derive the error bound of approximating least squares, ridge regression, and Lasso algorithms with transformer. The corresponding statistical properties, i.e., the convergence rate and Bayes suboptima... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback on our paper. We respond to the specific questions as follows.
> …rare experimental results are provided to corroborate these constructions. For example, the authors construct transformers that implement one step gradient descent with several layers... | Summary: In context Learning, is a setting in which Transformers can learn to perform new tasks when prompted with training and test examples. This work advances the understanding of the capabilities of ICL. In particular, this paper proves that for a broad class of standard machine learning algorithms like ridge regre... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback on our paper. We respond to the specific questions as follows.
> How results such as those in [1] compares to the one obtained in this paper.
Our approximation results and [1] are very different. [1] shows that transformers are universal approximat... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Toward Re-Identifying Any Animal | Accept (poster) | Summary: The paper focuses on the ReID problem. Different from previous works, which mainly focus on persons and vehicles, the paper wants to reID any animals. To realize this, the authors construct a new dataset that contains many animals. Also, the authors propose a visual guidance generator, a textual guidance gener... | Rebuttal 1:
Rebuttal: 1.Data:
For the open-world problem, there are two mainstream solutions. One is using huge dataset to train a strong deep model, such as SAM. The other one is transfer-learning methods, e.g., UniDetetcor [A]. These methods employ a dataset with a certain volume to gather knowledge and increase the ... | Summary: This paper introduces a new task called "Re-identify Any Animal in the Wild" (ReID-AW) which aims to develop a ReID model capable of handling any unseen wildlife category it encounters. To address this challenge, the authors created a comprehensive dataset called Wildlife-71, which includes ReID data from 71 d... | Rebuttal 1:
Rebuttal: 1.**Performance comparisons in seen categories.**\
To address this concern, we evaluated our UniReID model and the top-performing competitors, DTIN-Net [A], on two seen categories: alligator and morgan. Here, we need to clarify that we are continually augmenting our Wildlife-71 dataset and have am... | Summary: This paper proposes a new task called “Re-identify Any Animal in the Wild” (ReID-AW) and created a comprehensive dataset called Wildlife-71 which is used to evaluate ReID-AW methods. Furthermore, the authors present a universal re-identification model named UniReID specifically for the ReID-AW task. This model... | Rebuttal 1:
Rebuttal: 1.**Why techniques are necessary and why it is distinctive for animal**\
Sorry for causing confusion. Here, we need to clarify that the techniques encompassed within our model are not designed to fit animal objects but address the primary challenge of the ReID-AW task, i.e., identify instances wit... | Summary: This paper extends common person or vehicle reid task to re-identifying any animal in the wild, called ReID-AW task. First, authors propose an animal reid dataset for the ReID-AW task that contains 71 different categories. Then, authors also propose a new method for this task to tackle specific challenges in t... | Rebuttal 1:
Rebuttal: 1. **Evaluate the performance on other categories.**\
Thank you for your suggestion. To extend our test set, we have further incorporated new instances from two seen categories, *i.e.*, alligator and morgan, as well as from an unseen category named chameleon. In these three categories, our UniR... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Towards a Better Theoretical Understanding of Independent Subnetwork Training | Reject | Summary: The authors provided a theoretical analysis for independent subnet training and provided a convergence analysis in the case where communication compression is present.
The authors discussed the scenario when bias is not present and provided two analyses in the homogeneous and heterogeneous case, respectively ... | Rebuttal 1:
Rebuttal: Dear Reviewer LM8J,
Thanks for the time and effort devoted to our paper. We appreciate the positive evaluation of our work.
# Responses to questions
> The authors mentioned that prior work on the convergence of IST focus "on overparameterized single hidden layer neural networks with ReLU activat... | Summary: The paper provides a theoretical analysis of the convergence properties of Independent Subnetwork Training (IST), for distributed Stochastic Gradient Descent (SGD) optimization with a quadratic loss function. The analysis considers both the cases of homogeneous and heterogeneous distributed scenarios, without ... | Rebuttal 1:
Rebuttal: Dear Reviewer LM8J,
Thanks for the time and effort devoted to our paper. We greatly value careful reading of the material and the positive evaluation of our work.
# Responses to questions
> It would be valuable if the authors could consider a discussion or possible additional experiments to exte... | Summary: Independent Subnetwork Training (IST) is a technique that divides the neural network into smaller independent subnetworks, trains them in a distributed parallel way, and aggregates the results of each independent subnetwork to update the weights of the whole model.
This paper aims to analyze the behavior of IS... | Rebuttal 1:
Rebuttal: Dear Reviewer wecA,
Thanks for the time and effort devoted to our paper.
# Responses to questions
> How do the findings of this work be of any help to the future design of IST architecture? In addition, the authors should include more experiments on SOTA IST applications, e.g., ResNet, and Graph... | Summary: This submission presents a theoretical analysis for the independent subnetwork training (IST) algorithm for training models under both data and model parallel settings. The convergence guarantees are analyzed for quadratic loss functions, when using permutation sketches as the model compressors.
Strengths: - ... | Rebuttal 1:
Rebuttal: Dear reviewer 87cd,
Thanks for your time and effort. Let us respond to mentioned weaknesses and questions separately.
# Responses to questions
> Can the authors please detail how their choice of the quadratic loss function ties to practical applications of model parallelism (e.g. for neural netwo... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Firstly, we would like to thank the reviewers for their time reading our paper and for their feedback! This is all much appreciated.
We are encouraged that the considered problem is found _important and practical_, needing theoretical insights (R. 87cd). We are pleased that the l... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
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