id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.01701 | Learning with Differentially Private (Sliced) Wasserstein Gradients | cs.LG math.ST stat.TH | In this work, we introduce a novel framework for privately optimizing
objectives that rely on Wasserstein distances between data-dependent empirical
measures. Our main theoretical contribution is, based on an explicit
formulation of the Wasserstein gradient in a fully discrete setting, a control
on the sensitivity of... |
2502.01702 | Al-Khwarizmi: Discovering Physical Laws with Foundation Models | cs.LG | Inferring physical laws from data is a central challenge in science and
engineering, including but not limited to healthcare, physical sciences,
biosciences, social sciences, sustainability, climate, and robotics. Deep
networks offer high-accuracy results but lack interpretability, prompting
interest in models built ... |
2502.01703 | QLESS: A Quantized Approach for Data Valuation and Selection in Large
Language Model Fine-Tuning | cs.LG cs.AI cs.CL | Fine-tuning large language models (LLMs) is often constrained by the
computational costs of processing massive datasets. We propose \textbf{QLESS}
(Quantized Low-rank Gradient Similarity Search), which integrates gradient
quantization with the LESS framework to enable memory-efficient data valuation
and selection. QL... |
2502.01704 | Adaptive Observation Cost Control for Variational Quantum Eigensolvers | quant-ph cs.LG | The objective to be minimized in the variational quantum eigensolver (VQE)
has a restricted form, which allows a specialized sequential minimal
optimization (SMO) that requires only a few observations in each iteration.
However, the SMO iteration is still costly due to the observation noise -- one
observation at a po... |
2502.01705 | Progressive Binarization with Semi-Structured Pruning for LLMs | cs.LG | Large language models (LLMs) have achieved remarkable success in natural
language processing tasks, but their high computational and memory demands pose
challenges for deployment on resource-constrained devices. Binarization, as an
efficient compression method that reduces model weights to just 1 bit,
significantly l... |
2502.01706 | Comply: Learning Sentences with Complex Weights inspired by Fruit Fly
Olfaction | cs.CL cs.AI cs.LG cs.NE | Biologically inspired neural networks offer alternative avenues to model data
distributions. FlyVec is a recent example that draws inspiration from the fruit
fly's olfactory circuit to tackle the task of learning word embeddings.
Surprisingly, this model performs competitively even against deep learning
approaches sp... |
2502.01707 | CLIP-DQA: Blindly Evaluating Dehazed Images from Global and Local
Perspectives Using CLIP | cs.CV cs.AI | Blind dehazed image quality assessment (BDQA), which aims to accurately
predict the visual quality of dehazed images without any reference information,
is essential for the evaluation, comparison, and optimization of image dehazing
algorithms. Existing learning-based BDQA methods have achieved remarkable
success, whi... |
2502.01708 | Aspects of Artificial Intelligence: Transforming Machine Learning
Systems Naturally | cs.LG cs.AI cs.DB cs.DM | In this paper, we study the machine learning elements which we are interested
in together as a machine learning system, consisting of a collection of machine
learning elements and a collection of relations between the elements. The
relations we concern are algebraic operations, binary relations, and binary
relations ... |
2502.01709 | Adapter-Based Multi-Agent AVSR Extension for Pre-Trained ASR Models | cs.SD cs.LG eess.AS | We present an approach to Audio-Visual Speech Recognition that builds on a
pre-trained Whisper model. To infuse visual information into this audio-only
model, we extend it with an AV fusion module and LoRa adapters, one of the most
up-to-date adapter approaches. One advantage of adapter-based approaches, is
that only... |
2502.01710 | A Multi-Scale Feature Fusion Framework Integrating Frequency Domain and
Cross-View Attention for Dual-View X-ray Security Inspections | cs.CV | With the rapid development of modern transportation systems and the
exponential growth of logistics volumes, intelligent X-ray-based security
inspection systems play a crucial role in public safety. Although single-view
X-ray equipment is widely deployed, it struggles to accurately identify
contraband in complex stac... |
2502.01711 | Expected Return Symmetries | cs.MA | Symmetry is an important inductive bias that can improve model robustness and
generalization across many deep learning domains. In multi-agent settings, a
priori known symmetries have been shown to address a fundamental coordination
failure mode known as mutually incompatible symmetry breaking; e.g. in a game
where t... |
2502.01713 | Auditing a Dutch Public Sector Risk Profiling Algorithm Using an
Unsupervised Bias Detection Tool | cs.CY cs.LG | Algorithms are increasingly used to automate or aid human decisions, yet
recent research shows that these algorithms may exhibit bias across legally
protected demographic groups. However, data on these groups may be unavailable
to organizations or external auditors due to privacy legislation. This paper
studies bias ... |
2502.01714 | Position: Towards a Responsible LLM-empowered Multi-Agent Systems | cs.MA cs.AI | The rise of Agent AI and Large Language Model-powered Multi-Agent Systems
(LLM-MAS) has underscored the need for responsible and dependable system
operation. Tools like LangChain and Retrieval-Augmented Generation have
expanded LLM capabilities, enabling deeper integration into MAS through
enhanced knowledge retrieva... |
2502.01715 | Process-Supervised Reinforcement Learning for Code Generation | cs.SE cs.AI | Existing reinforcement learning strategies based on outcome supervision have
proven effective in enhancing the performance of large language models(LLMs)
for code generation. While reinforcement learning based on process supervision
has shown great promise in handling multi-step reasoning tasks, its
effectiveness in ... |
2502.01717 | Choose Your Model Size: Any Compression by a Single Gradient Descent | cs.LG | The adoption of Foundation Models in resource-constrained environments
remains challenging due to their large size and inference costs. A promising
way to overcome these limitations is post-training compression, which aims to
balance reduced model size against performance degradation. This work presents
Any Compressi... |
2502.01718 | ACECODER: Acing Coder RL via Automated Test-Case Synthesis | cs.SE cs.AI cs.CL | Most progress in recent coder models has been driven by supervised
fine-tuning (SFT), while the potential of reinforcement learning (RL) remains
largely unexplored, primarily due to the lack of reliable reward data/model in
the code domain. In this paper, we address this challenge by leveraging
automated large-scale ... |
2502.01719 | MJ-VIDEO: Fine-Grained Benchmarking and Rewarding Video Preferences in
Video Generation | cs.CV | Recent advancements in video generation have significantly improved the
ability to synthesize videos from text instructions. However, existing models
still struggle with key challenges such as instruction misalignment, content
hallucination, safety concerns, and bias. Addressing these limitations, we
introduce MJ-BEN... |
2502.01720 | Generating Multi-Image Synthetic Data for Text-to-Image Customization | cs.CV cs.GR cs.LG | Customization of text-to-image models enables users to insert custom concepts
and generate the concepts in unseen settings. Existing methods either rely on
costly test-time optimization or train encoders on single-image training
datasets without multi-image supervision, leading to worse image quality. We
propose a si... |
2502.01739 | Grokking vs. Learning: Same Features, Different Encodings | cs.LG cond-mat.dis-nn cs.AI | Grokking typically achieves similar loss to ordinary, "steady", learning. We
ask whether these different learning paths - grokking versus ordinary training
- lead to fundamental differences in the learned models. To do so we compare
the features, compressibility, and learning dynamics of models trained via each
path ... |
2502.01754 | Evaluation of Large Language Models via Coupled Token Generation | cs.CL cs.AI cs.LG | State of the art large language models rely on randomization to respond to a
prompt. As an immediate consequence, a model may respond differently to the
same prompt if asked multiple times. In this work, we argue that the evaluation
and ranking of large language models should control for the randomization
underpinnin... |
2502.01755 | Robust Federated Finetuning of LLMs via Alternating Optimization of LoRA | cs.LG cs.AI | Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation
(LoRA) optimize federated training by reducing computational and communication
costs. We propose RoLoRA, a federated framework using alternating optimization
to fine-tune LoRA adapters. Our approach emphasizes the importance of learning
up and do... |
2502.01763 | On The Concurrence of Layer-wise Preconditioning Methods and Provable
Feature Learning | cs.LG math.OC stat.ML | Layer-wise preconditioning methods are a family of memory-efficient
optimization algorithms that introduce preconditioners per axis of each layer's
weight tensors. These methods have seen a recent resurgence, demonstrating
impressive performance relative to entry-wise ("diagonal") preconditioning
methods such as Adam... |
2502.01770 | Hamming Attention Distillation: Binarizing Keys and Queries for
Efficient Long-Context Transformers | cs.LG cs.AI eess.IV | Pre-trained transformer models with extended context windows are notoriously
expensive to run at scale, often limiting real-world deployment due to their
high computational and memory requirements. In this paper, we introduce Hamming
Attention Distillation (HAD), a novel framework that binarizes keys and queries
in t... |
2502.01772 | On Bob Dylan: A Computational Perspective | cs.CL cs.AI cs.IR cs.SI | Cass Sunstein's essay 'On Bob Dylan' describes Dylan's 'dishabituating' style
-- a constant refusal to conform to expectation and a penchant for reinventing
his musical and lyrical identity. In this paper, I extend Sunstein's
observations through a large-scale computational analysis of Dylan's lyrics
from 1962 to 201... |
2502.01773 | Coarse-to-Fine 3D Keyframe Transporter | cs.RO cs.CV | Recent advances in Keyframe Imitation Learning (IL) have enabled
learning-based agents to solve a diverse range of manipulation tasks. However,
most approaches ignore the rich symmetries in the problem setting and, as a
consequence, are sample-inefficient. This work identifies and utilizes the
bi-equivariant symmetry... |
2502.01774 | Grokking Explained: A Statistical Phenomenon | cs.LG cs.AI | Grokking, or delayed generalization, is an intriguing learning phenomenon
where test set loss decreases sharply only after a model's training set loss
has converged. This challenges conventional understanding of the training
dynamics in deep learning networks. In this paper, we formalize and investigate
grokking, hig... |
2502.01776 | Sparse VideoGen: Accelerating Video Diffusion Transformers with
Spatial-Temporal Sparsity | cs.CV cs.LG | Diffusion Transformers (DiTs) dominate video generation but their high
computational cost severely limits real-world applicability, usually requiring
tens of minutes to generate a few seconds of video even on high-performance
GPUs. This inefficiency primarily arises from the quadratic computational
complexity of 3D F... |
2502.01777 | CTC-DRO: Robust Optimization for Reducing Language Disparities in Speech
Recognition | cs.LG cs.CL eess.AS | Modern deep learning models often achieve high overall performance, but
consistently fail on specific subgroups. Group distributionally robust
optimization (group DRO) addresses this problem by minimizing the worst-group
loss, but it fails when group losses misrepresent performance differences
between groups. This is... |
2502.01778 | GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient
Optimization in Dynamic Environments | cs.LG cs.SY eess.SY | Reinforcement Learning (RL) methods used for solving real-world optimization
problems often involve dynamic state-action spaces, larger scale, and sparse
rewards, leading to significant challenges in convergence, scalability, and
efficient exploration of the solution space. This study introduces GNN-DT, a
novel Decis... |
2502.01780 | Graph Canonical Correlation Analysis | stat.ML cs.LG | Canonical correlation analysis (CCA) is a widely used technique for
estimating associations between two sets of multi-dimensional variables. Recent
advancements in CCA methods have expanded their application to decipher the
interactions of multiomics datasets, imaging-omics datasets, and more. However,
conventional C... |
2502.01784 | VILP: Imitation Learning with Latent Video Planning | cs.RO cs.CV | In the era of generative AI, integrating video generation models into
robotics opens new possibilities for the general-purpose robot agent. This
paper introduces imitation learning with latent video planning (VILP). We
propose a latent video diffusion model to generate predictive robot videos that
adhere to temporal ... |
2502.01785 | AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene
Analysis | cs.CV cs.AI | The preservation of aquatic biodiversity is critical in mitigating the
effects of climate change. Aquatic scene understanding plays a pivotal role in
aiding marine scientists in their decision-making processes. In this paper, we
introduce AquaticCLIP, a novel contrastive language-image pre-training model
tailored for... |
2502.01787 | The Effects of Enterprise Social Media on Communication Networks | cs.CY cs.SI | Enterprise social media platforms (ESMPs) are web-based platforms with
standard social media functionality, e.g., communicating with others, posting
links and files, liking content, etc., yet all users are part of the same
company. The first contribution of this work is the use of a
difference-in-differences analysis... |
2502.01789 | An Agentic AI Workflow for Detecting Cognitive Concerns in Real-world
Data | cs.AI cs.MA | Early identification of cognitive concerns is critical but often hindered by
subtle symptom presentation. This study developed and validated a fully
automated, multi-agent AI workflow using LLaMA 3 8B to identify cognitive
concerns in 3,338 clinical notes from Mass General Brigham. The agentic
workflow, leveraging ta... |
2502.01792 | Policy Design for Two-sided Platforms with Participation Dynamics | cs.GT cs.IR cs.LG cs.SY eess.SY | In two-sided platforms (e.g., video streaming or e-commerce), viewers and
providers engage in interactive dynamics, where an increased provider
population results in higher viewer utility and the increase of viewer
population results in higher provider utility. Despite the importance of such
"population effects" on l... |
2502.01800 | Flow-based Domain Randomization for Learning and Sequencing Robotic
Skills | cs.RO cs.AI cs.LG | Domain randomization in reinforcement learning is an established technique
for increasing the robustness of control policies trained in simulation. By
randomizing environment properties during training, the learned policy can
become robust to uncertainties along the randomized dimensions. While the
environment distri... |
2502.01803 | Discovering Chunks in Neural Embeddings for Interpretability | cs.LG cs.AI | Understanding neural networks is challenging due to their high-dimensional,
interacting components. Inspired by human cognition, which processes complex
sensory data by chunking it into recurring entities, we propose leveraging this
principle to interpret artificial neural population activities. Biological and
artifi... |
2502.01804 | Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging | cs.LG cs.CL | Machine learning models are routinely trained on a mixture of different data
domains. Different domain weights yield very different downstream performances.
We propose the Soup-of-Experts, a novel architecture that can instantiate a
model at test time for any domain weights with minimal computational cost and
without... |
2502.01806 | Toward Neurosymbolic Program Comprehension | cs.SE cs.AI | Recent advancements in Large Language Models (LLMs) have paved the way for
Large Code Models (LCMs), enabling automation in complex software engineering
tasks, such as code generation, software testing, and program comprehension,
among others. Tools like GitHub Copilot and ChatGPT have shown substantial
benefits in s... |
2502.01809 | Self-supervised Subgraph Neural Network With Deep Reinforcement Walk
Exploration | cs.LG | Graph data, with its structurally variable nature, represents complex
real-world phenomena like chemical compounds, protein structures, and social
networks. Traditional Graph Neural Networks (GNNs) primarily utilize the
message-passing mechanism, but their expressive power is limited and their
prediction lacks explai... |
2502.01810 | Estimating Network Models using Neural Networks | cs.SI econ.EM stat.CO stat.ML | Exponential random graph models (ERGMs) are very flexible for modeling
network formation but pose difficult estimation challenges due to their
intractable normalizing constant. Existing methods, such as MCMC-MLE, rely on
sequential simulation at every optimization step. We propose a neural network
approach that train... |
2502.01812 | SelfCheckAgent: Zero-Resource Hallucination Detection in Generative
Large Language Models | cs.CL cs.LG | Detecting hallucinations in Large Language Models (LLMs) remains a critical
challenge for their reliable deployment in real-world applications. To address
this, we introduce SelfCheckAgent, a novel framework integrating three
different agents: the Symbolic Agent, the Specialized Detection Agent, and the
Contextual Co... |
2502.01814 | PolyhedronNet: Representation Learning for Polyhedra with
Surface-attributed Graph | cs.CV cs.LG | Ubiquitous geometric objects can be precisely and efficiently represented as
polyhedra. The transformation of a polyhedron into a vector, known as polyhedra
representation learning, is crucial for manipulating these shapes with
mathematical and statistical tools for tasks like classification, clustering,
and generati... |
2502.01816 | Low Resource Video Super-resolution using Memory and Residual Deformable
Convolutions | cs.CV cs.LG | Transformer-based video super-resolution (VSR) models have set new benchmarks
in recent years, but their substantial computational demands make most of them
unsuitable for deployment on resource-constrained devices. Achieving a balance
between model complexity and output quality remains a formidable challenge in
VSR.... |
2502.01819 | Score as Action: Fine-Tuning Diffusion Generative Models by
Continuous-time Reinforcement Learning | cs.LG cs.AI math.OC | Reinforcement learning from human feedback (RLHF), which aligns a diffusion
model with input prompt, has become a crucial step in building reliable
generative AI models. Most works in this area use a discrete-time formulation,
which is prone to induced errors, and often not applicable to models with
higher-order/blac... |
2502.01820 | Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks
in Laser Powder Bed Fusion | cs.CE | Modeling plays a critical role in additive manufacturing (AM), enabling a
deeper understanding of underlying processes. Parametric solutions for such
models are of great importance, enabling the optimization of production
processes and considerable cost reductions. However, the complexity of the
problem and diversity... |
2502.01821 | Agentic Bug Reproduction for Effective Automated Program Repair at
Google | cs.SE cs.AI | Bug reports often lack sufficient detail for developers to reproduce and fix
the underlying defects. Bug Reproduction Tests (BRTs), tests that fail when the
bug is present and pass when it has been resolved, are crucial for debugging,
but they are rarely included in bug reports, both in open-source and in
industrial ... |
2502.01825 | Assessing Data Augmentation-Induced Bias in Training and Testing of
Machine Learning Models | cs.SE cs.AI | Data augmentation has become a standard practice in software engineering to
address limited or imbalanced data sets, particularly in specialized domains
like test classification and bug detection where data can be scarce. Although
techniques such as SMOTE and mutation-based augmentation are widely used in
software te... |
2502.01827 | Relatively-Secure LLM-Based Steganography via Constrained Markov
Decision Processes | cs.IT math.IT | Linguistic steganography aims to conceal information within natural language
text without being detected. An effective steganography approach should encode
the secret message into a minimal number of language tokens while preserving
the natural appearance and fluidity of the stego-texts. We present a new
framework to... |
2502.01828 | From Foresight to Forethought: VLM-In-the-Loop Policy Steering via
Latent Alignment | cs.RO cs.LG | While generative robot policies have demonstrated significant potential in
learning complex, multimodal behaviors from demonstrations, they still exhibit
diverse failures at deployment-time. Policy steering offers an elegant solution
to reducing the chance of failure by using an external verifier to select from
low-l... |
2502.01830 | Meta-neural Topology Optimization: Knowledge Infusion with Meta-learning | cs.CE physics.comp-ph | Engineers learn from every design they create, building intuition that helps
them quickly identify promising solutions for new problems. Topology
optimization (TO) - a well-established computational method for designing
structures with optimized performance - lacks this ability to learn from
experience. Existing appr... |
2502.01834 | Building a Cognitive Twin Using a Distributed Cognitive System and an
Evolution Strategy | cs.AI cs.NE | This work presents a technique to build interaction-based Cognitive Twins (a
computational version of an external agent) using input-output training and an
Evolution Strategy on top of a framework for distributed Cognitive
Architectures. Here, we show that it's possible to orchestrate many simple
physical and virtual... |
2502.01836 | LeaFi: Data Series Indexes on Steroids with Learned Filters | cs.DB | The ever-growing collections of data series create a pressing need for
efficient similarity search, which serves as the backbone for various analytics
pipelines. Recent studies have shown that tree-based series indexes excel in
many scenarios. However, we observe a significant waste of effort during
search, due to su... |
2502.01837 | TESS: A Scalable Temporally and Spatially Local Learning Rule for
Spiking Neural Networks | cs.NE cs.AI cs.LG | The demand for low-power inference and training of deep neural networks
(DNNs) on edge devices has intensified the need for algorithms that are both
scalable and energy-efficient. While spiking neural networks (SNNs) allow for
efficient inference by processing complex spatio-temporal dynamics in an
event-driven fashi... |
2502.01839 | Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling
Verification | cs.LG cs.AI | Sampling-based search, a simple paradigm for utilizing test-time compute,
involves generating multiple candidate responses and selecting the best one --
typically by having models self-verify each response for correctness. In this
paper, we study the scaling trends governing sampling-based search. Among our
findings ... |
2502.01842 | Texture Image Synthesis Using Spatial GAN Based on Vision Transformers | cs.CV cs.AI | Texture synthesis is a fundamental task in computer vision, whose goal is to
generate visually realistic and structurally coherent textures for a wide range
of applications, from graphics to scientific simulations. While traditional
methods like tiling and patch-based techniques often struggle with complex
textures, ... |
2502.01846 | UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping | cs.CV | 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D
objects and scenes. However, generating 3DGS remains challenging due to their
discrete, unstructured, and permutation-invariant nature. In this work, we
present a simple yet effective method to overcome these challenges. We utilize
spherica... |
2502.01847 | Containment Control Approach for Steering Opinion in a Social Network | eess.SY cs.MA cs.SY math.DS math.OC | The paper studies the problem of steering multi-dimensional opinion in a
social network. Assuming the society of desire consists of stubborn and regular
agents, stubborn agents are considered as leaders who specify the desired
opinion distribution as a distributed reward or utility function. In this
context, each reg... |
2502.01850 | Foundation Model-Based Apple Ripeness and Size Estimation for Selective
Harvesting | cs.CV | Harvesting is a critical task in the tree fruit industry, demanding extensive
manual labor and substantial costs, and exposing workers to potential hazards.
Recent advances in automated harvesting offer a promising solution by enabling
efficient, cost-effective, and ergonomic fruit picking within tight harvesting
win... |
2502.01853 | Security and Quality in LLM-Generated Code: A Multi-Language,
Multi-Model Analysis | cs.CR cs.LG cs.SE | Artificial Intelligence (AI)-driven code generation tools are increasingly
used throughout the software development lifecycle to accelerate coding tasks.
However, the security of AI-generated code using Large Language Models (LLMs)
remains underexplored, with studies revealing various risks and weaknesses.
This paper... |
2502.01854 | How to warm-start your unfolding network | cs.LG eess.IV eess.SP | We present a new ensemble framework for boosting the performance of
overparameterized unfolding networks solving the compressed sensing problem. We
combine a state-of-the-art overparameterized unfolding network with a
continuation technique, to warm-start a crucial quantity of the said network's
architecture; we coin... |
2502.01855 | Learning Fine-to-Coarse Cuboid Shape Abstraction | cs.CV cs.GR | The abstraction of 3D objects with simple geometric primitives like cuboids
allows to infer structural information from complex geometry. It is important
for 3D shape understanding, structural analysis and geometric modeling. We
introduce a novel fine-to-coarse unsupervised learning approach to abstract
collections o... |
2502.01856 | Reliability-Driven LiDAR-Camera Fusion for Robust 3D Object Detection | cs.CV cs.LG | Accurate and robust 3D object detection is essential for autonomous driving,
where fusing data from sensors like LiDAR and camera enhances detection
accuracy. However, sensor malfunctions such as corruption or disconnection can
degrade performance, and existing fusion models often struggle to maintain
reliability whe... |
2502.01857 | Learning Human Perception Dynamics for Informative Robot Communication | cs.RO cs.AI | Human-robot cooperative navigation is challenging in environments with
incomplete information. We introduce CoNav-Maze, a simulated robotics
environment where a robot navigates using local perception while a human
operator provides guidance based on an inaccurate map. The robot can share its
camera views to improve t... |
2502.01858 | Rethinking Energy Management for Autonomous Ground Robots on a Budget | cs.RO cs.SY eess.SY | Autonomous Ground Robots (AGRs) face significant challenges due to limited
energy reserve, which restricts their overall performance and availability.
Prior research has focused separately on energy-efficient approaches and fleet
management strategies for task allocation to extend operational time. A
fleet-level sche... |
2502.01860 | SE Arena: Benchmarking Software Engineering Chatbots with Iterative
Interactions | cs.SE cs.LG | Foundation models (FMs), particularly large language models (LLMs), have
shown significant promise in various software engineering (SE) tasks, including
code generation, debugging, and requirement refinement. Despite these advances,
existing evaluation frameworks are insufficient for assessing model performance
in it... |
2502.01861 | Learning Hyperparameters via a Data-Emphasized Variational Objective | cs.LG stat.ML | When training large flexible models, practitioners often rely on grid search
to select hyperparameters that control over-fitting. This grid search has
several disadvantages: the search is computationally expensive, requires
carving out a validation set that reduces the available data for training, and
requires users ... |
2502.01865 | Enhancing Generalization via Sharpness-Aware Trajectory Matching for
Dataset Condensation | cs.LG | Dataset condensation aims to synthesize datasets with a few representative
samples that can effectively represent the original datasets. This enables
efficient training and produces models with performance close to those trained
on the original sets. Most existing dataset condensation methods conduct
dataset learning... |
2502.01866 | Online Curvature-Aware Replay: Leveraging $\mathbf{2^{nd}}$ Order
Information for Online Continual Learning | cs.LG cs.AI | Online Continual Learning (OCL) models continuously adapt to nonstationary
data streams, usually without task information. These settings are complex and
many traditional CL methods fail, while online methods (mainly replay-based)
suffer from instabilities after the task shift. To address this issue, we
formalize rep... |
2502.01867 | Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the
Cold Start Problem under Auction Dynamics | cs.LG | Online advertising platforms often face a common challenge: the cold start
problem. Insufficient behavioral data (clicks) makes accurate click-through
rate (CTR) forecasting of new ads challenging. CTR for "old" items can also be
significantly underestimated due to their early performance influencing their
long-term ... |
2502.01873 | Explaining Automatic Image Assessment | cs.CV | Previous work in aesthetic categorization and explainability utilizes manual
labeling and classification to explain aesthetic scores. These methods require
a complex labeling process and are limited in size. Our proposed approach
attempts to explain aesthetic assessment models through visualizing dataset
trends and a... |
2502.01874 | Countering Election Sway: Strategic Algorithms in Friedkin-Johnsen
Dynamics | cs.SI | Social influence profoundly impacts individual choices and collective
behaviors in politics. In this work, driven by the goal of protecting elections
from improper influence, we consider the following scenario: an individual, who
has vested interests in political party $Y$, is aware through reliable surveys
that part... |
2502.01876 | Reinforcement Learning with Segment Feedback | cs.LG | Standard reinforcement learning (RL) assumes that an agent can observe a
reward for each state-action pair. However, in practical applications, it is
often difficult and costly to collect a reward for each state-action pair.
While there have been several works considering RL with trajectory feedback, it
is unclear if... |
2502.01882 | Latent Lexical Projection in Large Language Models: A Novel Approach to
Implicit Representation Refinement | cs.CL | Generating semantically coherent text requires a robust internal
representation of linguistic structures, which traditional embedding techniques
often fail to capture adequately. A novel approach, Latent Lexical Projection
(LLP), is introduced to refine lexical representations through a structured
transformation into... |
2502.01885 | A Privacy-Preserving Domain Adversarial Federated learning for
multi-site brain functional connectivity analysis | cs.LG cs.AI eess.IV | Resting-state functional magnetic resonance imaging (rs-fMRI) and its derived
functional connectivity networks (FCNs) have become critical for understanding
neurological disorders. However, collaborative analyses and the
generalizability of models still face significant challenges due to privacy
regulations and the n... |
2502.01889 | Displacement-Sparse Neural Optimal Transport | cs.LG cs.AI | Optimal Transport (OT) theory seeks to determine the map $T:X \to Y$ that
transports a source measure $P$ to a target measure $Q$, minimizing the cost
$c(\mathbf{x}, T(\mathbf{x}))$ between $\mathbf{x}$ and its image
$T(\mathbf{x})$. Building upon the Input Convex Neural Network OT solver and
incorporating the concep... |
2502.01890 | Geometric Framework for 3D Cell Segmentation Correction | cs.CV cs.LG | 3D cellular image segmentation methods are commonly divided into non-2D-based
and 2D-based approaches, the latter reconstructing 3D shapes from the
segmentation results of 2D layers. However, errors in 2D results often
propagate, leading to oversegmentations in the final 3D results. To tackle this
issue, we introduce... |
2502.01891 | Training and Evaluating with Human Label Variation: An Empirical Study | cs.LG cs.CL | Human label variation (HLV) challenges the standard assumption that an
example has a single ground truth, instead embracing the natural variation in
human labelling to train and evaluate models. While various training methods
and metrics for HLV have been proposed, there has been no systematic
meta-evaluation of HLV ... |
2502.01894 | SimBEV: A Synthetic Multi-Task Multi-Sensor Driving Data Generation Tool
and Dataset | cs.CV cs.LG cs.RO | Bird's-eye view (BEV) perception for autonomous driving has garnered
significant attention in recent years, in part because BEV representation
facilitates the fusion of multi-sensor data. This enables a variety of
perception tasks including BEV segmentation, a concise view of the environment
that can be used to plan ... |
2502.01896 | INTACT: Inducing Noise Tolerance through Adversarial Curriculum Training
for LiDAR-based Safety-Critical Perception and Autonomy | cs.CV cs.RO | In this work, we present INTACT, a novel two-phase framework designed to
enhance the robustness of deep neural networks (DNNs) against noisy LiDAR data
in safety-critical perception tasks. INTACT combines meta-learning with
adversarial curriculum training (ACT) to systematically address challenges
posed by data corru... |
2502.01901 | Conceptual Metaphor Theory as a Prompting Paradigm for Large Language
Models | cs.CL | We introduce Conceptual Metaphor Theory (CMT) as a framework for enhancing
large language models (LLMs) through cognitive prompting in complex reasoning
tasks. CMT leverages metaphorical mappings to structure abstract reasoning,
improving models' ability to process and explain intricate concepts. By
incorporating CMT... |
2502.01904 | Common Neighborhood Estimation over Bipartite Graphs under Local
Differential Privacy | cs.DB | Bipartite graphs, formed by two vertex layers, arise as a natural fit for
modeling the relationships between two groups of entities. In bipartite graphs,
common neighborhood computation between two vertices on the same vertex layer
is a basic operator, which is easily solvable in general settings. However, it
inevita... |
2502.01905 | When not to target negative ties? Studying competitive influence
maximisation in signed networks | cs.SI | We explore the influence maximisation problem in networks with negative ties.
Where prior work has focused on unsigned networks, we investigate the need to
consider negative ties in networks while trying to maximise spread in a
population - particularly under competitive conditions. Given a signed network
we optimise... |
2502.01906 | Rethinking Homogeneity of Vision and Text Tokens in Large
Vision-and-Language Models | cs.CV | Large vision-and-language models (LVLMs) typically treat visual and textual
embeddings as homogeneous inputs to a large language model (LLM). However,
these inputs are inherently different: visual inputs are multi-dimensional and
contextually rich, often pre-encoded by models like CLIP, while textual inputs
lack this... |
2502.01908 | Unlocking Efficient Large Inference Models: One-Bit Unrolling Tips the
Scales | cs.LG | Recent advancements in Large Language Model (LLM) compression, such as BitNet
and BitNet b1.58, have marked significant strides in reducing the computational
demands of LLMs through innovative one-bit quantization techniques. We extend
this frontier by looking at Large Inference Models (LIMs) that have become
indispe... |
2502.01912 | PATCH: a deep learning method to assess heterogeneity of artistic
practice in historical paintings | cs.CV cs.AI cs.LG | The history of art has seen significant shifts in the manner in which
artworks are created, making understanding of creative processes a central
question in technical art history. In the Renaissance and Early Modern period,
paintings were largely produced by master painters directing workshops of
apprentices who ofte... |
2502.01913 | Composite Gaussian Processes Flows for Learning Discontinuous Multimodal
Policies | cs.RO cs.LG | Learning control policies for real-world robotic tasks often involve
challenges such as multimodality, local discontinuities, and the need for
computational efficiency. These challenges arise from the complexity of robotic
environments, where multiple solutions may coexist. To address these issues, we
propose Composi... |
2502.01916 | Generalizable and Fast Surrogates: Model Predictive Control of
Articulated Soft Robots using Physics-Informed Neural Networks | cs.RO cs.LG | Soft robots can revolutionize several applications with high demands on
dexterity and safety. When operating these systems, real-time estimation and
control require fast and accurate models. However, prediction with
first-principles (FP) models is slow, and learned black-box models have poor
generalizability. Physics... |
2502.01918 | Wake-Informed 3D Path Planning for Autonomous Underwater Vehicles Using
A* and Neural Network Approximations | cs.RO cs.AI cs.LG | Autonomous Underwater Vehicles (AUVs) encounter significant energy, control
and navigation challenges in complex underwater environments, particularly
during close-proximity operations, such as launch and recovery (LAR), where
fluid interactions and wake effects present additional navigational and energy
challenges. ... |
2502.01919 | Poisson Hierarchical Indian Buffet Processes for Within and Across Group
Sharing of Latent Features-With Indications for Microbiome Species Sampling
Models | stat.ML cs.LG math.PR math.ST stat.TH | In this work, we present a comprehensive Bayesian posterior analysis of what
we term Poisson Hierarchical Indian Buffet Processes, designed for complex
random sparse count species sampling models that allow for the sharing of
information across and within groups. This analysis covers a potentially
infinite number of ... |
2502.01920 | Anomaly Detection via Autoencoder Composite Features and NCE | cs.LG | Unsupervised anomaly detection is a challenging task. Autoencoders (AEs) or
generative models are often employed to model the data distribution of normal
inputs and subsequently identify anomalous, out-of-distribution inputs by high
reconstruction error or low likelihood, respectively. However, AEs may
generalize and... |
2502.01922 | LAST SToP For Modeling Asynchronous Time Series | cs.LG cs.AI | We present a novel prompt design for Large Language Models (LLMs) tailored to
Asynchronous Time Series. Unlike regular time series, which assume values at
evenly spaced time points, asynchronous time series consist of timestamped
events occurring at irregular intervals, each described in natural language.
Our approac... |
2502.01924 | DualGuard MPPI: Safe and Performant Optimal Control by Combining
Sampling-Based MPC and Hamilton-Jacobi Reachability | eess.SY cs.RO cs.SY | Designing controllers that are both safe and performant is inherently
challenging. This co-optimization can be formulated as a constrained optimal
control problem, where the cost function represents the performance criterion
and safety is specified as a constraint. While sampling-based methods, such as
Model Predicti... |
2502.01925 | PANDAS: Improving Many-shot Jailbreaking via Positive Affirmation,
Negative Demonstration, and Adaptive Sampling | cs.CL cs.CR cs.LG | Many-shot jailbreaking circumvents the safety alignment of large language
models by exploiting their ability to process long input sequences. To achieve
this, the malicious target prompt is prefixed with hundreds of fabricated
conversational turns between the user and the model. These fabricated exchanges
are randoml... |
2502.01926 | Fairness through Difference Awareness: Measuring Desired Group
Discrimination in LLMs | cs.CY cs.CL | Algorithmic fairness has conventionally adopted a perspective of racial
color-blindness (i.e., difference unaware treatment). We contend that in a
range of important settings, group difference awareness matters. For example,
differentiating between groups may be necessary in legal contexts (e.g., the
U.S. compulsory ... |
2502.01930 | Distributionally Robust Direct Preference Optimization | cs.LG cs.AI | A major challenge in aligning large language models (LLMs) with human
preferences is the issue of distribution shift. LLM alignment algorithms rely
on static preference datasets, assuming that they accurately represent
real-world user preferences. However, user preferences vary significantly
across geographical regio... |
2502.01932 | VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion
Control and Strategic Play | cs.RO cs.AI cs.LG | Multi-agent reinforcement learning (MARL) has made significant progress,
largely fueled by the development of specialized testbeds that enable
systematic evaluation of algorithms in controlled yet challenging scenarios.
However, existing testbeds often focus on purely virtual simulations or limited
robot morphologies... |
2502.01936 | Query-Based and Unnoticeable Graph Injection Attack from Neighborhood
Perspective | cs.LG cs.CR | The robustness of Graph Neural Networks (GNNs) has become an increasingly
important topic due to their expanding range of applications. Various attack
methods have been proposed to explore the vulnerabilities of GNNs, ranging from
Graph Modification Attacks (GMA) to the more practical and flexible Graph
Injection Att... |
2502.01937 | A Comprehensive Study of Bug-Fix Patterns in Autonomous Driving Systems | cs.SE cs.RO | As autonomous driving systems (ADSes) become increasingly complex and
integral to daily life, the importance of understanding the nature and
mitigation of software bugs in these systems has grown correspondingly.
Addressing the challenges of software maintenance in autonomous driving systems
(e.g., handling real-time... |
2502.01940 | Toward a Low-Cost Perception System in Autonomous Vehicles: A Spectrum
Learning Approach | cs.CV eess.IV | We present a cost-effective new approach for generating denser depth maps for
Autonomous Driving (AD) and Autonomous Vehicles (AVs) by integrating the images
obtained from deep neural network (DNN) 4D radar detectors with conventional
camera RGB images. Our approach introduces a novel pixel positional encoding
algori... |
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