id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.08378 | Learning Humanoid Standing-up Control across Diverse Postures | cs.RO cs.AI cs.LG | Standing-up control is crucial for humanoid robots, with the potential for
integration into current locomotion and loco-manipulation systems, such as fall
recovery. Existing approaches are either limited to simulations that overlook
hardware constraints or rely on predefined ground-specific motion trajectories,
faili... |
2502.08391 | ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for
Whole Slide Image Classification | cs.CV | Multiple instance learning (MIL)-based framework has become the mainstream
for processing the whole slide image (WSI) with giga-pixel size and
hierarchical image context in digital pathology. However, these methods heavily
depend on a substantial number of bag-level labels and solely learn from the
original slides, w... |
2502.08395 | IssueBench: Millions of Realistic Prompts for Measuring Issue Bias in
LLM Writing Assistance | cs.CL | Large language models (LLMs) are helping millions of users write texts about
diverse issues, and in doing so expose users to different ideas and
perspectives. This creates concerns about issue bias, where an LLM tends to
present just one perspective on a given issue, which in turn may influence how
users think about ... |
2502.08397 | Strong bounds for large-scale Minimum Sum-of-Squares Clustering | math.OC cs.LG | Clustering is a fundamental technique in data analysis and machine learning,
used to group similar data points together. Among various clustering methods,
the Minimum Sum-of-Squares Clustering (MSSC) is one of the most widely used.
MSSC aims to minimize the total squared Euclidean distance between data points
and the... |
2502.08404 | Quantifying Collective Emotions: Japan's Societal Trends Through
Enhanced Sentiment Index Using POMS2 and SNS | cs.SI cs.CY | In this study, we constructed an emotion index that quantitatively represents
the collective emotions present in the Japanese web space by utilizing Social
Networking Service (SNS) post data. Building upon previous research that used
blog data and the Profile of Mood States (POMS), we restructured the
methodology usi... |
2502.08414 | Sparse Estimation of Inverse Covariance and Partial Correlation Matrices
via Joint Partial Regression | stat.ML cs.LG | We present a new method for estimating high-dimensional sparse partial
correlation and inverse covariance matrices, which exploits the connection
between the inverse covariance matrix and linear regression. The method is a
two-stage estimation method wherein each individual feature is regressed on all
other features ... |
2502.08415 | A Semantic Parsing Algorithm to Solve Linear Ordering Problems | cs.CL cs.LO | We develop an algorithm to semantically parse linear ordering problems, which
require a model to arrange entities using deductive reasoning. Our method takes
as input a number of premises and candidate statements, parsing them to a
first-order logic of an ordering domain, and then utilizes constraint logic
programmin... |
2502.08416 | Multifidelity Simulation-based Inference for Computationally Expensive
Simulators | stat.ML cs.LG | Across many domains of science, stochastic models are an essential tool to
understand the mechanisms underlying empirically observed data. Models can be
of different levels of detail and accuracy, with models of high-fidelity (i.e.,
high accuracy) to the phenomena under study being often preferable. However,
inferrin... |
2502.08417 | Handwritten Text Recognition: A Survey | cs.CV cs.AI | Handwritten Text Recognition (HTR) has become an essential field within
pattern recognition and machine learning, with applications spanning historical
document preservation to modern data entry and accessibility solutions. The
complexity of HTR lies in the high variability of handwriting, which makes it
challenging ... |
2502.08426 | Semantic Learning for Molecular Communication in Internet of Bio-Nano
Things | eess.SP cs.ET cs.LG eess.IV | Molecular communication (MC) provides a foundational framework for
information transmission in the Internet of Bio-Nano Things (IoBNT), where
efficiency and reliability are crucial. However, the inherent limitations of
molecular channels, such as low transmission rates, noise, and inter-symbol
interference (ISI), lim... |
2502.08428 | Robot-Initiated Social Control of Sedentary Behavior: Comparing the
Impact of Relationship- and Target-Focused Strategies | cs.HC cs.RO | To design social robots to effectively promote health behavior change, it is
essential to understand how people respond to various health communication
strategies employed by these robots. This study examines the effectiveness of
two types of social control strategies from a social robot,
relationship-focused strateg... |
2502.08432 | Closer through commonality: Enhancing hypergraph contrastive learning
with shared groups | cs.LG | Hypergraphs provide a superior modeling framework for representing complex
multidimensional relationships in the context of real-world interactions that
often occur in groups, overcoming the limitations of traditional homogeneous
graphs. However, there have been few studies on hypergraphbased contrastive
learning, an... |
2502.08436 | From Haystack to Needle: Label Space Reduction for Zero-shot
Classification | cs.CL cs.AI cs.LG | We present Label Space Reduction (LSR), a novel method for improving
zero-shot classification performance of Large Language Models (LLMs). LSR
iteratively refines the classification label space by systematically ranking
and reducing candidate classes, enabling the model to concentrate on the most
relevant options. By... |
2502.08438 | Composite Sketch+Text Queries for Retrieving Objects with Elusive Names
and Complex Interactions | cs.CV cs.AI cs.CL cs.IR cs.MM | Non-native speakers with limited vocabulary often struggle to name specific
objects despite being able to visualize them, e.g., people outside Australia
searching for numbats. Further, users may want to search for such elusive
objects with difficult-to-sketch interactions, e.g., numbat digging in the
ground. In such ... |
2502.08441 | Better Embeddings with Coupled Adam | cs.CL cs.AI cs.LG | Despite their remarkable capabilities, LLMs learn word representations that
exhibit the undesirable yet poorly understood feature of anisotropy. In this
paper, we argue that the second moment in Adam is a cause of anisotropic
embeddings, and suggest a modified optimizer called Coupled Adam to mitigate
the problem. Ou... |
2502.08445 | LucidAtlas$: Learning Uncertainty-Aware, Covariate-Disentangled,
Individualized Atlas Representations | cs.LG | The goal of this work is to develop principled techniques to extract
information from high dimensional data sets with complex dependencies in areas
such as medicine that can provide insight into individual as well as population
level variation. We develop $\texttt{LucidAtlas}$, an approach that can
represent spatiall... |
2502.08448 | Monge SAM: Robust Reparameterization-Invariant Sharpness-Aware
Minimization Based on Loss Geometry | cs.LG stat.ML | Recent studies on deep neural networks show that flat minima of the loss
landscape correlate with improved generalization. Sharpness-aware minimization
(SAM) efficiently finds flat regions by updating the parameters according to
the gradient at an adversarial perturbation. The perturbation depends on the
Euclidean me... |
2502.08449 | CordViP: Correspondence-based Visuomotor Policy for Dexterous
Manipulation in Real-World | cs.RO cs.AI | Achieving human-level dexterity in robots is a key objective in the field of
robotic manipulation. Recent advancements in 3D-based imitation learning have
shown promising results, providing an effective pathway to achieve this goal.
However, obtaining high-quality 3D representations presents two key problems:
(1) the... |
2502.08450 | Towards Prompt Generalization: Grammar-aware Cross-Prompt Automated
Essay Scoring | cs.CL cs.AI | In automated essay scoring (AES), recent efforts have shifted toward
cross-prompt settings that score essays on unseen prompts for practical
applicability. However, prior methods trained with essay-score pairs of
specific prompts pose challenges in obtaining prompt-generalized essay
representation. In this work, we p... |
2502.08452 | Learning to Group and Grasp Multiple Objects | cs.RO | Simultaneously grasping and transporting multiple objects can significantly
enhance robotic work efficiency and has been a key research focus for decades.
The primary challenge lies in determining how to push objects, group them, and
execute simultaneous grasping for respective groups while considering object
distrib... |
2502.08453 | Proceedings 40th International Conference on Logic Programming | cs.LO cs.AI | Since the first conference In Marseille in 1982, the International Conference
on Logic Programming (ICLP) has been the premier international event for
presenting research in logic programming. These proceedings include technical
communications about, and abstracts for presentations given at the 40th ICLP
held October... |
2502.08455 | Resilient Quantized Consensus in Multi-Hop Relay Networks | cs.MA cs.SY eess.SY | We study resilient quantized consensus in multi-agent systems, where some
agents may malfunction. The network consists of agents taking integer-valued
states, and the agents' communication is subject to asynchronous updates and
time delays. We utilize the quantized weighted mean subsequence reduced
algorithm where ag... |
2502.08457 | Learning Theory for Kernel Bilevel Optimization | cs.LG | Bilevel optimization has emerged as a technique for addressing a wide range
of machine learning problems that involve an outer objective implicitly
determined by the minimizer of an inner problem. In this paper, we investigate
the generalization properties for kernel bilevel optimization problems where
the inner obje... |
2502.08458 | Examining Spanish Counseling with MIDAS: a Motivational Interviewing
Dataset in Spanish | cs.CL | Cultural and language factors significantly influence counseling, but Natural
Language Processing research has not yet examined whether the findings of
conversational analysis for counseling conducted in English apply to other
languages. This paper presents a first step towards this direction. We
introduce MIDAS (Mot... |
2502.08468 | mmE5: Improving Multimodal Multilingual Embeddings via High-quality
Synthetic Data | cs.CV cs.AI cs.CL | Multimodal embedding models have gained significant attention for their
ability to map data from different modalities, such as text and images, into a
unified representation space. However, the limited labeled multimodal data
often hinders embedding performance. Recent approaches have leveraged data
synthesis to addr... |
2502.08470 | Numerical Schemes for Signature Kernels | math.NA cs.LG cs.NA math.AP | Signature kernels have emerged as a powerful tool within kernel methods for
sequential data. In the paper "The Signature Kernel is the solution of a
Goursat PDE", the authors identify a kernel trick that demonstrates that, for
continuously differentiable paths, the signature kernel satisfies a Goursat
problem for a h... |
2502.08474 | Training-Free Restoration of Pruned Neural Networks | cs.LG cs.AI cs.CV | Although network pruning has been highly popularized to compress deep neural
networks, its resulting accuracy heavily depends on a fine-tuning process that
is often computationally expensive and requires the original data. However,
this may not be the case in real-world scenarios, and hence a few recent works
attempt... |
2502.08482 | Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned
Reasoning | cs.CL cs.AI cs.LG | Chain-of-Thought (CoT) prompting has emerged as a powerful technique for
enhancing language model's reasoning capabilities. However, generating long and
correct CoT trajectories is challenging. Recent studies have demonstrated that
Looped Transformers possess remarkable length generalization capabilities, but
their l... |
2502.08486 | Referring Remote Sensing Image Segmentation via Bidirectional Alignment
Guided Joint Prediction | cs.CV | Referring Remote Sensing Image Segmentation (RRSIS) is critical for
ecological monitoring, urban planning, and disaster management, requiring
precise segmentation of objects in remote sensing imagery guided by textual
descriptions. This task is uniquely challenging due to the considerable
vision-language gap, the hig... |
2502.08488 | One-Shot Federated Learning with Classifier-Free Diffusion Models | cs.LG | Federated learning (FL) enables collaborative learning without data
centralization but introduces significant communication costs due to multiple
communication rounds between clients and the server. One-shot federated
learning (OSFL) addresses this by forming a global model with a single
communication round, often re... |
2502.08489 | Salamandra Technical Report | cs.CL | This work introduces Salamandra, a suite of open-source decoder-only large
language models available in three different sizes: 2, 7, and 40 billion
parameters. The models were trained from scratch on highly multilingual data
that comprises text in 35 European languages and code. Our carefully curated
corpus is made e... |
2502.08490 | Flat-Top Beamforming with Efficient Array-Fed RIS | eess.SP cs.SY eess.SY | Flat-top beam designs are essential for uniform power distribution over a
wide angular sector for applications such as 5G/6G networks, satellite
communications, radar systems, etc. Low sidelobe levels with steep transitions
allow negligible cross sector illumination. Active array designs requiring
amplitude taper suf... |
2502.08496 | Fine-Tuning Topics through Weighting Aspect Keywords | cs.IR cs.LG | Topic modeling often requires examining topics from multiple perspectives to
uncover hidden patterns, especially in less explored areas. This paper presents
an approach to address this need, utilizing weighted keywords from various
aspects derived from a domain knowledge. The research method starts with
standard topi... |
2502.08502 | On the Fundamental Limits of Integrated Sensing and Communications Under
Logarithmic Loss | cs.IT math.IT | We study a unified information-theoretic framework for integrated sensing and
communications (ISAC), applicable to both monostatic and bistatic sensing
scenarios. Special attention is given to the case where the sensing receiver
(Rx) is required to produce a "soft" estimate of the state sequence, with
logarithmic los... |
2502.08503 | Revisiting 3D LLM Benchmarks: Are We Really Testing 3D Capabilities? | cs.AI | In this work, we identify the "2D-Cheating" problem in 3D LLM evaluation,
where these tasks might be easily solved by VLMs with rendered images of point
clouds, exposing ineffective evaluation of 3D LLMs' unique 3D capabilities. We
test VLM performance across multiple 3D LLM benchmarks and, using this as a
reference,... |
2502.08505 | Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based
Framework for Effective Label Propagation | cs.LG | Graph Neural Networks (GNNs) have recently become the predominant tools for
studying graph data. Despite state-of-the-art performance on graph
classification tasks, GNNs are overwhelmingly trained in a single domain under
supervision, thus necessitating a prohibitively high demand for labels and
resulting in poorly t... |
2502.08507 | Explanation based In-Context Demonstrations Retrieval for Multilingual
Grammatical Error Correction | cs.CL | Grammatical error correction (GEC) aims to correct grammatical, spelling, and
semantic errors in natural language text. With the growing of large language
models (LLMs), direct text generation has gradually become the focus of the GEC
methods, and few-shot in-context learning presents a cost-effective solution.
Howev... |
2502.08512 | Measuring Diversity in Synthetic Datasets | cs.CL cs.AI | Large language models (LLMs) are widely adopted to generate synthetic
datasets for various natural language processing (NLP) tasks, such as text
classification and summarization. However, accurately measuring the diversity
of these synthetic datasets-an aspect crucial for robust model
performance-remains a significan... |
2502.08514 | Faithful, Unfaithful or Ambiguous? Multi-Agent Debate with Initial
Stance for Summary Evaluation | cs.CL | Faithfulness evaluators based on large language models (LLMs) are often
fooled by the fluency of the text and struggle with identifying errors in the
summaries. We propose an approach to summary faithfulness evaluation in which
multiple LLM-based agents are assigned initial stances (regardless of what
their belief mi... |
2502.08515 | The Paradox of Stochasticity: Limited Creativity and Computational
Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data | cs.LG | This study examines how temperature settings and model architectures affect
the generation of structured fictional data (names, birthdates) across three
large language models (LLMs): llama3.1:8b, deepseek-r1:8b, and mistral:latest.
By systematically testing temperature values from 0.0 to 1.0 in increments of
0.1, we ... |
2502.08518 | FedMHO: Heterogeneous One-Shot Federated Learning Towards
Resource-Constrained Edge Devices | cs.LG cs.AI cs.DC | Federated Learning (FL) is increasingly adopted in edge computing scenarios,
where a large number of heterogeneous clients operate under constrained or
sufficient resources. The iterative training process in conventional FL
introduces significant computation and communication overhead, which is
unfriendly for resourc... |
2502.08522 | Abstract questionnaires and FS-decision digraphs | math.CO cs.IT math.IT math.ST stat.TH | A questionnaire is a sequence of multiple choice questions aiming to collect
data on a population. We define an abstract questionnaire as an ordered pair
$(N,{\cal M})$, where $N$ is a positive integer and ${\cal
M}=(m_0,m_1,\ldots,m_{N-1})$ is an $N$-tuple of positive integers, with $m_i$,
for $i \in \{0, 1, \ldots,... |
2502.08524 | LLM Pretraining with Continuous Concepts | cs.LG cs.CL | Next token prediction has been the standard training objective used in large
language model pretraining. Representations are learned as a result of
optimizing for token-level perplexity. We propose Continuous Concept Mixing
(CoCoMix), a novel pretraining framework that combines discrete next token
prediction with con... |
2502.08525 | Checkerboard Target Measurement in Unordered Point Clouds with Coloured
ICP | cs.CE | In this work, we investigate the problem of measuring a the centre
checkerboard target in an 3D point cloud. This is an important problem which
has applications in registration, long term monitoring and linking to other
sensor systems. We use a 3D template matching approach based on the coloured
ICP algorithm to solv... |
2502.08528 | BCDDM: Branch-Corrected Denoising Diffusion Model for Black Hole Image
Generation | astro-ph.GA cs.CV | The properties of black holes and accretion flows can be inferred by fitting
Event Horizon Telescope (EHT) data to simulated images generated through
general relativistic ray tracing (GRRT). However, due to the computationally
intensive nature of GRRT, the efficiency of generating specific radiation flux
images needs... |
2502.08529 | Testbed Development: An Intelligent O-RAN based Cell-Free MIMO Network | cs.NI cs.SY eess.SY | Cell-free multiple input multiple output (CF-MIMO) systems improve spectral
and energy efficiencies using distributed access points (APs) to provide
reliable service across an area equivalent to multiple conventional cells. This
paper presents a novel design and implementation of a CF-MIMO network
leveraging the open... |
2502.08531 | On Different Notions of Redundancy in Conditional-Independence-Based
Discovery of Graphical Models | cs.LG stat.ML | The goal of conditional-independence-based discovery of graphical models is
to find a graph that represents the independence structure of variables in a
given dataset. To learn such a representation, conditional-independence-based
approaches conduct a set of statistical tests that suffices to identify the
graphical r... |
2502.08534 | Input convex neural networks: universal approximation theorem and
implementation for isotropic polyconvex hyperelastic energies | cs.CE cs.AI | This paper presents a novel framework of neural networks for isotropic
hyperelasticity that enforces necessary physical and mathematical constraints
while simultaneously satisfying the universal approximation theorem. The two
key ingredients are an input convex network architecture and a formulation in
the elementary... |
2502.08536 | Matrix Completion with Graph Information: A Provable Nonconvex
Optimization Approach | cs.LG math.OC | We consider the problem of matrix completion with graphs as side information
depicting the interrelations between variables. The key challenge lies in
leveraging the similarity structure of the graph to enhance matrix recovery.
Existing approaches, primarily based on graph Laplacian regularization, suffer
from severa... |
2502.08540 | A Survey on Image Quality Assessment: Insights, Analysis, and Future
Outlook | cs.CV | Image quality assessment (IQA) represents a pivotal challenge in
image-focused technologies, significantly influencing the advancement
trajectory of image processing and computer vision. Recently, IQA has witnessed
a notable surge in innovative research efforts, driven by the emergence of
novel architectural paradigm... |
2502.08542 | Beyond Predictions: A Participatory Framework for Multi-Stakeholder
Decision-Making | cs.LG cs.MA | Conventional decision-support systems, primarily based on supervised
learning, focus on outcome prediction models to recommend actions. However,
they often fail to account for the complexities of multi-actor environments,
where diverse and potentially conflicting stakeholder preferences must be
balanced. In this pape... |
2502.08544 | Moment of Untruth: Dealing with Negative Queries in Video Moment
Retrieval | cs.CV | Video Moment Retrieval is a common task to evaluate the performance of
visual-language models - it involves localising start and end times of moments
in videos from query sentences. The current task formulation assumes that the
queried moment is present in the video, resulting in false positive moment
predictions whe... |
2502.08547 | Representation Learning to Advance Multi-institutional Studies with
Electronic Health Record Data | cs.AI | The adoption of EHRs has expanded opportunities to leverage data-driven
algorithms in clinical care and research. A major bottleneck in effectively
conducting multi-institutional EHR studies is the data heterogeneity across
systems with numerous codes that either do not exist or represent different
clinical concepts ... |
2502.08549 | Copula-based mixture model identification for subgroup clustering with
imaging applications | cs.CV cs.LG | Model-based clustering techniques have been widely applied to various
application areas, while most studies focus on canonical mixtures with unique
component distribution form. However, this strict assumption is often hard to
satisfy. In this paper, we consider the more flexible Copula-Based Mixture
Models (CBMMs) fo... |
2502.08550 | LLMs can implicitly learn from mistakes in-context | cs.CL cs.AI | Learning from mistakes is a fundamental feature of human intelligence.
Previous work has shown that Large Language Models (LLMs) can also learn from
incorrect answers when provided with a comprehensive rationale detailing why an
answer is wrong or how to correct it. In this work, we examine whether LLMs can
learn fro... |
2502.08554 | Fostering Appropriate Reliance on Large Language Models: The Role of
Explanations, Sources, and Inconsistencies | cs.HC cs.AI | Large language models (LLMs) can produce erroneous responses that sound
fluent and convincing, raising the risk that users will rely on these responses
as if they were correct. Mitigating such overreliance is a key challenge.
Through a think-aloud study in which participants use an LLM-infused
application to answer o... |
2502.08555 | A Machine Learning-Ready Data Processing Tool for Near Real-Time
Forecasting | astro-ph.SR astro-ph.IM cs.LG | Space weather forecasting is critical for mitigating radiation risks in space
exploration and protecting Earth-based technologies from geomagnetic
disturbances. This paper presents the development of a Machine Learning (ML)-
ready data processing tool for Near Real-Time (NRT) space weather forecasting.
By merging dat... |
2502.08556 | Human-Centric Foundation Models: Perception, Generation and Agentic
Modeling | cs.CV cs.AI cs.LG cs.MM | Human understanding and generation are critical for modeling digital humans
and humanoid embodiments. Recently, Human-centric Foundation Models (HcFMs)
inspired by the success of generalist models, such as large language and vision
models, have emerged to unify diverse human-centric tasks into a single
framework, sur... |
2502.08557 | QA-Expand: Multi-Question Answer Generation for Enhanced Query Expansion
in Information Retrieval | cs.IR cs.CL cs.LG cs.MA | Query expansion is widely used in Information Retrieval (IR) to improve
search outcomes by enriching queries with additional contextual information.
Although recent Large Language Model (LLM) based methods generate
pseudo-relevant content and expanded terms via multiple prompts, they often
yield repetitive, narrow ex... |
2502.08560 | Brain Latent Progression: Individual-based Spatiotemporal Disease
Progression on 3D Brain MRIs via Latent Diffusion | cs.CV cs.AI | The growing availability of longitudinal Magnetic Resonance Imaging (MRI)
datasets has facilitated Artificial Intelligence (AI)-driven modeling of
disease progression, making it possible to predict future medical scans for
individual patients. However, despite significant advancements in AI, current
methods continue ... |
2502.08561 | Quality-Aware Decoding: Unifying Quality Estimation and Decoding | cs.CL | Quality Estimation (QE) models for Neural Machine Translation (NMT) predict
the quality of the hypothesis without having access to the reference. An
emerging research direction in NMT involves the use of QE models, which have
demonstrated high correlations with human judgment and can enhance translations
through Qual... |
2502.08566 | AR Glulam: Accurate Augmented Reality Using Multiple Fiducial Markers
for Glulam Fabrication | cs.ET cs.CV cs.HC | Recent advancements in Augmented Reality (AR) have demonstrated applications
in architecture, design, and fabrication. Compared to conventional 2D
construction drawings, AR can be used to superimpose contextual instructions,
display 3D spatial information and enable on-site engagement. Despite the
potential of AR, th... |
2502.08573 | A Novel Approach to for Multimodal Emotion Recognition : Multimodal
semantic information fusion | cs.CV cs.AI | With the advancement of artificial intelligence and computer vision
technologies, multimodal emotion recognition has become a prominent research
topic. However, existing methods face challenges such as heterogeneous data
fusion and the effective utilization of modality correlations. This paper
proposes a novel multim... |
2502.08574 | COAST: Intelligent Time-Adaptive Neural Operators | cs.LG cs.AI | We introduce Causal Operator with Adaptive Solver Transformer (COAST), a
novel neural operator learning method that leverages a causal language model
(CLM) framework to dynamically adapt time steps. Our method predicts both the
evolution of a system and its optimal time step, intelligently balancing
computational eff... |
2502.08576 | Mapping the Landscape of Generative AI in Network Monitoring and
Management | cs.NI cs.AI cs.LG | Generative Artificial Intelligence (GenAI) models such as LLMs, GPTs, and
Diffusion Models have recently gained widespread attention from both the
research and the industrial communities. This survey explores their application
in network monitoring and management, focusing on prominent use cases, as well
as challenge... |
2502.08577 | FBFL: A Field-Based Coordination Approach for Data Heterogeneity in
Federated Learning | cs.LG cs.AI | In the last years, Federated learning (FL) has become a popular solution to
train machine learning models in domains with high privacy concerns. However,
FL scalability and performance face significant challenges in real-world
deployments where data across devices are non-independently and identically
distributed (no... |
2502.08580 | Ultrasound Image Generation using Latent Diffusion Models | cs.CV | Diffusion models for image generation have been a subject of increasing
interest due to their ability to generate diverse, high-quality images. Image
generation has immense potential in medical imaging because open-source medical
images are difficult to obtain compared to natural images, especially for rare
condition... |
2502.08582 | A method for classification of data with uncertainty using hypothesis
testing | cs.LG | Binary classification is a task that involves the classification of data into
one of two distinct classes. It is widely utilized in various fields. However,
conventional classifiers tend to make overconfident predictions for data that
belong to overlapping regions of the two class distributions or for data
outside th... |
2502.08585 | Scalable Bilevel Loss Balancing for Multi-Task Learning | cs.LG | Multi-task learning (MTL) has been widely adopted for its ability to
simultaneously learn multiple tasks. While existing gradient manipulation
methods often yield more balanced solutions than simple scalarization-based
approaches, they typically incur a significant computational overhead of
$\mathcal{O}(K)$ in both t... |
2502.08586 | Commercial LLM Agents Are Already Vulnerable to Simple Yet Dangerous
Attacks | cs.LG cs.AI | A high volume of recent ML security literature focuses on attacks against
aligned large language models (LLMs). These attacks may extract private
information or coerce the model into producing harmful outputs. In real-world
deployments, LLMs are often part of a larger agentic pipeline including memory
systems, retrie... |
2502.08590 | Light-A-Video: Training-free Video Relighting via Progressive Light
Fusion | cs.CV | Recent advancements in image relighting models, driven by large-scale
datasets and pre-trained diffusion models, have enabled the imposition of
consistent lighting. However, video relighting still lags, primarily due to the
excessive training costs and the scarcity of diverse, high-quality video
relighting datasets. ... |
2502.08593 | Toward Universal Laws of Outlier Propagation | cs.LG | We argue that Algorithmic Information Theory (AIT) admits a principled way to
quantify outliers in terms of so-called randomness deficiency. For the
probability distribution generated by a causal Bayesian network, we show that
the randomness deficiency of the joint state decomposes into randomness
deficiencies of eac... |
2502.08597 | Learning in Markets with Heterogeneous Agents: Dynamics and Survival of
Bayesian vs. No-Regret Learners | cs.GT cs.AI cs.MA econ.TH | We analyze the performance of heterogeneous learning agents in asset markets
with stochastic payoffs. Our agents aim to maximize the expected growth rate of
their wealth but have different theories on how to learn this best. We focus on
comparing Bayesian and no-regret learners in market dynamics. Bayesian learners
w... |
2502.08598 | Enhancing Diffusion Models Efficiency by Disentangling Total-Variance
and Signal-to-Noise Ratio | cs.LG stat.ML | The long sampling time of diffusion models remains a significant bottleneck,
which can be mitigated by reducing the number of diffusion time steps. However,
the quality of samples with fewer steps is highly dependent on the noise
schedule, i.e., the specific manner in which noise is introduced and the signal
is reduc... |
2502.08599 | SPeCtrum: A Grounded Framework for Multidimensional Identity
Representation in LLM-Based Agent | cs.CL | Existing methods for simulating individual identities often oversimplify
human complexity, which may lead to incomplete or flattened representations. To
address this, we introduce SPeCtrum, a grounded framework for constructing
authentic LLM agent personas by incorporating an individual's multidimensional
self-concep... |
2502.08600 | Two-stage hybrid models for enhancing forecasting accuracy on
heterogeneous time series | cs.LG | Compared to local models built in a series-by-series manner, global models
leverage relevant information across time series, resulting in improved
forecasting performance and generalization capacity. Constructing global models
on a set of time series is becoming mainstream in the field of time series
forecasting. How... |
2502.08603 | Scalable Thermodynamic Second-order Optimization | cs.ET cs.LG | Many hardware proposals have aimed to accelerate inference in AI workloads.
Less attention has been paid to hardware acceleration of training, despite the
enormous societal impact of rapid training of AI models. Physics-based
computers, such as thermodynamic computers, offer an efficient means to solve
key primitives... |
2502.08605 | CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection | cs.LG cs.AI | Does the intrinsic curvature of complex networks hold the key to unveiling
graph anomalies that conventional approaches overlook? Reconstruction-based
graph anomaly detection (GAD) methods overlook such geometric outliers,
focusing only on structural and attribute-level anomalies. To this end, we
propose CurvGAD - a ... |
2502.08606 | Distillation Scaling Laws | cs.LG cs.AI cs.CL stat.ML | We provide a distillation scaling law that estimates distilled model
performance based on a compute budget and its allocation between the student
and teacher. Our findings reduce the risks associated with using distillation
at scale; compute allocation for both the teacher and student models can now be
done to maximi... |
2502.08610 | Quantifying Security Vulnerabilities: A Metric-Driven Security Analysis
of Gaps in Current AI Standards | cs.CR cs.AI | As AI systems integrate into critical infrastructure, security gaps in AI
compliance frameworks demand urgent attention. This paper audits and quantifies
security risks in three major AI governance standards: NIST AI RMF 1.0, UK's AI
and Data Protection Risk Toolkit, and the EU's ALTAI. Using a novel risk
assessment ... |
2502.08611 | Robustly Learning Monotone Generalized Linear Models via Data
Augmentation | cs.LG math.OC math.ST stat.TH | We study the task of learning Generalized Linear models (GLMs) in the
agnostic model under the Gaussian distribution. We give the first
polynomial-time algorithm that achieves a constant-factor approximation for
\textit{any} monotone Lipschitz activation. Prior constant-factor GLM learners
succeed for a substantially... |
2502.08612 | Continuous Cardiac Arrest Prediction in ICU using PPG Foundation Model | cs.LG | Non-invasive patient monitoring for tracking and predicting adverse acute
health events is an emerging area of research. We pursue in-hospital cardiac
arrest (IHCA) prediction using only single-channel finger photoplethysmography
(PPG) signals. Our proposed two-stage model Feature Extractor-Aggregator
Network (FEAN) ... |
2502.08620 | Mathematical Data Science | math.HO cs.LG math.CO math.NT math.RT | Can machine learning help discover new mathematical structures? In this
article we discuss an approach to doing this which one can call "mathematical
data science". In this paradigm, one studies mathematical objects collectively
rather than individually, by creating datasets and doing machine learning
experiments and... |
2502.08622 | Forecasting Drought Using Machine Learning in California | cs.LG | Drought is a frequent and costly natural disaster in California, with major
negative impacts on agricultural production and water resource availability,
particularly groundwater. This study investigated the performance of applying
different machine learning approaches to predicting the U.S. Drought Monitor
classifica... |
2502.08623 | Robot Data Curation with Mutual Information Estimators | cs.RO | The performance of imitation learning policies often hinges on the datasets
with which they are trained. Consequently, investment in data collection for
robotics has grown across both industrial and academic labs. However, despite
the marked increase in the quantity of demonstrations collected, little work
has sought... |
2502.08625 | Randomness of Low-Layer Parameters Determines Confusing Samples in Terms
of Interaction Representations of a DNN | cs.LG cs.AI cs.CL cs.CV | In this paper, we find that the complexity of interactions encoded by a deep
neural network (DNN) can explain its generalization power. We also discover
that the confusing samples of a DNN, which are represented by non-generalizable
interactions, are determined by its low-layer parameters. In comparison, other
factor... |
2502.08628 | Concentration Inequalities for the Stochastic Optimization of Unbounded
Objectives with Application to Denoising Score Matching | stat.ML cs.LG | We derive novel concentration inequalities that bound the statistical error
for a large class of stochastic optimization problems, focusing on the case of
unbounded objective functions. Our derivations utilize the following tools: 1)
A new form of McDiarmid's inequality that is based on sample dependent one
component... |
2502.08631 | Ensemble based approach to quantifying uncertainty of LLM based
classifications | cs.AI | The output of Large Language Models (LLMs) are a function of the internal
model's parameters and the input provided into the context window. The
hypothesis presented here is that under a greedy sampling strategy the variance
in the LLM's output is a function of the conceptual certainty embedded in the
model's paramet... |
2502.08632 | Necessary and Sufficient Oracles: Toward a Computational Taxonomy For
Reinforcement Learning | cs.LG cs.CC | Algorithms for reinforcement learning (RL) in large state spaces crucially
rely on supervised learning subroutines to estimate objects such as value
functions or transition probabilities. Since only the simplest supervised
learning problems can be solved provably and efficiently, practical performance
of an RL algori... |
2502.08634 | Rapid Whole Brain Mesoscale In-vivo MR Imaging using Multi-scale
Implicit Neural Representation | eess.IV cs.CV cs.LG | Purpose: To develop and validate a novel image reconstruction technique using
implicit neural representations (INR) for multi-view thick-slice acquisitions
while reducing the scan time but maintaining high signal-to-noise ratio (SNR).
Methods: We propose Rotating-view super-resolution (ROVER)-MRI, an unsupervised
neu... |
2502.08636 | PulseCheck457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large
Multimodal Models | cs.CV | Although large multimodal models (LMMs) have demonstrated remarkable
capabilities in visual scene interpretation and reasoning, their capacity for
complex and precise 3-dimensional spatial reasoning remains uncertain. Existing
benchmarks focus predominantly on 2D spatial understanding and lack a framework
to comprehe... |
2502.08637 | Joint Transmit and Pinching Beamforming for PASS: Optimization-Based or
Learning-Based? | eess.SP cs.IT cs.LG math.IT | A novel pinching antenna system (PASS)-enabled downlink multi-user
multiple-input single-output (MISO) framework is proposed. PASS consists of
multiple waveguides spanning over thousands of wavelength, which equip numerous
low-cost dielectric particles, named pinching antennas (PAs), to radiate
signals into free spac... |
2502.08638 | Examining Multilingual Embedding Models Cross-Lingually Through
LLM-Generated Adversarial Examples | cs.CL | The evaluation of cross-lingual semantic search capabilities of models is
often limited to existing datasets from tasks such as information retrieval and
semantic textual similarity. To allow for domain-specific evaluation, we
introduce Cross Lingual Semantic Discrimination (CLSD), a novel cross-lingual
semantic sear... |
2502.08639 | CineMaster: A 3D-Aware and Controllable Framework for Cinematic
Text-to-Video Generation | cs.CV | In this work, we present CineMaster, a novel framework for 3D-aware and
controllable text-to-video generation. Our goal is to empower users with
comparable controllability as professional film directors: precise placement of
objects within the scene, flexible manipulation of both objects and camera in
3D space, and i... |
2502.08640 | Utility Engineering: Analyzing and Controlling Emergent Value Systems in
AIs | cs.LG cs.AI cs.CL cs.CV cs.CY | As AIs rapidly advance and become more agentic, the risk they pose is
governed not only by their capabilities but increasingly by their propensities,
including goals and values. Tracking the emergence of goals and values has
proven a longstanding problem, and despite much interest over the years it
remains unclear wh... |
2502.08642 | SwiftSketch: A Diffusion Model for Image-to-Vector Sketch Generation | cs.CV | Recent advancements in large vision-language models have enabled highly
expressive and diverse vector sketch generation. However, state-of-the-art
methods rely on a time-consuming optimization process involving repeated
feedback from a pretrained model to determine stroke placement. Consequently,
despite producing im... |
2502.08643 | A Real-to-Sim-to-Real Approach to Robotic Manipulation with
VLM-Generated Iterative Keypoint Rewards | cs.RO cs.AI cs.CV | Task specification for robotic manipulation in open-world environments is
challenging, requiring flexible and adaptive objectives that align with human
intentions and can evolve through iterative feedback. We introduce Iterative
Keypoint Reward (IKER), a visually grounded, Python-based reward function that
serves as ... |
2502.08644 | Rhythmic sharing: A bio-inspired paradigm for zero-shot adaptation and
learning in neural networks | cs.LG cs.AI math.DS nlin.AO physics.bio-ph | The brain can rapidly adapt to new contexts and learn from limited data, a
coveted characteristic that artificial intelligence algorithms have struggled
to mimic. Inspired by oscillatory rhythms of the mechanical structures of
neural cells, we developed a learning paradigm that is based on oscillations in
link streng... |
2502.08645 | Re$^3$Sim: Generating High-Fidelity Simulation Data via
3D-Photorealistic Real-to-Sim for Robotic Manipulation | cs.RO | Real-world data collection for robotics is costly and resource-intensive,
requiring skilled operators and expensive hardware. Simulations offer a
scalable alternative but often fail to achieve sim-to-real generalization due
to geometric and visual gaps. To address these challenges, we propose a
3D-photorealistic real... |
2502.08646 | Poly-Autoregressive Prediction for Modeling Interactions | cs.CV | We introduce a simple framework for predicting the behavior of an agent in
multi-agent settings. In contrast to autoregressive (AR) tasks, such as
language processing, our focus is on scenarios with multiple agents whose
interactions are shaped by physical constraints and internal motivations. To
this end, we propose... |
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