id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.07851 | Fast and Safe Scheduling of Robots | [
"cs.RO"
] | In this paper, we present an experimental analysis of a fast heuristic algorithm that was designed to generate a fast, collision-free schedule for a set of robots on a path graph. The experiments confirm the algorithm's effectiveness in producing collision-free schedules as well as achieving the optimal solution when a... | {
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2502.07852 | Fresh2comm: Information Freshness Optimized Collaborative Perception | [
"cs.MA"
] | Collaborative perception is a cornerstone of intelligent connected vehicles, enabling them to share and integrate sensory data to enhance situational awareness. However, measuring the impact of high transmission delay and inconsistent delay on collaborative perception in real communication scenarios, as well as improvi... | {
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2502.07853 | PolicySimEval: A Benchmark for Evaluating Policy Outcomes through
Agent-Based Simulation | [
"cs.MA",
"cs.CY"
] | With the growing adoption of agent-based models in policy evaluation, a pressing question arises: Can such systems effectively simulate and analyze complex social scenarios to inform policy decisions? Addressing this challenge could significantly enhance the policy-making process, offering researchers and practitioners... | {
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2502.07854 | Advancing Heat Demand Forecasting with Attention Mechanisms:
Opportunities and Challenges | [
"cs.LG",
"cs.CV"
] | Global leaders and policymakers are unified in their unequivocal commitment to decarbonization efforts in support of Net-Zero agreements. District Heating Systems (DHS), while contributing to carbon emissions due to the continued reliance on fossil fuels for heat production, are embracing more sustainable practices alb... | {
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2502.07855 | Vision-Language Models for Edge Networks: A Comprehensive Survey | [
"cs.CV",
"cs.AI",
"cs.CL"
] | Vision Large Language Models (VLMs) combine visual understanding with natural language processing, enabling tasks like image captioning, visual question answering, and video analysis. While VLMs show impressive capabilities across domains such as autonomous vehicles, smart surveillance, and healthcare, their deployment... | {
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2502.07856 | MRS: A Fast Sampler for Mean Reverting Diffusion based on ODE and SDE
Solvers | [
"cs.CV",
"cs.AI",
"cs.LG"
] | In applications of diffusion models, controllable generation is of practical significance, but is also challenging. Current methods for controllable generation primarily focus on modifying the score function of diffusion models, while Mean Reverting (MR) Diffusion directly modifies the structure of the stochastic diffe... | {
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2502.07857 | SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect
Estimation With an Unknown Graph | [
"stat.ML",
"cs.AI",
"cs.LG"
] | Causal discovery can be computationally demanding for large numbers of variables. If we only wish to estimate the causal effects on a small subset of target variables, we might not need to learn the causal graph for all variables, but only a small subgraph that includes the targets and their adjustment sets. In this pa... | {
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2502.07858 | MAAT: Mamba Adaptive Anomaly Transformer with association discrepancy
for time series | [
"cs.LG"
] | Anomaly detection in time series is essential for industrial monitoring and environmental sensing, yet distinguishing anomalies from complex patterns remains challenging. Existing methods like the Anomaly Transformer and DCdetector have progressed, but they face limitations such as sensitivity to short-term contexts an... | {
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2502.07859 | Automatic Prostate Volume Estimation in Transabdominal Ultrasound Images | [
"eess.IV",
"cs.CV"
] | Prostate cancer is a leading health concern among men, requiring accurate and accessible methods for early detection and risk stratification. Prostate volume (PV) is a key parameter in multivariate risk stratification for early prostate cancer detection, commonly estimated using transrectal ultrasound (TRUS). While TRU... | {
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2502.07861 | BalanceKV: KV Cache Compression through Discrepancy Theory | [
"cs.LG",
"cs.AI",
"cs.DS"
] | Large language models (LLMs) have achieved impressive success, but their high memory requirements present challenges for long-context token generation. The memory complexity of long-context LLMs is primarily due to the need to store Key-Value (KV) embeddings in their KV cache. We present BalanceKV, a KV cache compressi... | {
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2502.07862 | ADMN: A Layer-Wise Adaptive Multimodal Network for Dynamic Input Noise
and Compute Resources | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Multimodal deep learning systems are deployed in dynamic scenarios due to the robustness afforded by multiple sensing modalities. Nevertheless, they struggle with varying compute resource availability (due to multi-tenancy, device heterogeneity, etc.) and fluctuating quality of inputs (from sensor feed corruption, envi... | {
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2502.07864 | TransMLA: Multi-Head Latent Attention Is All You Need | [
"cs.LG",
"cs.AI"
] | Modern large language models (LLMs) often encounter communication bottlenecks on current hardware, rather than purely computational constraints. Multi-head Latent Attention (MLA) tackles this challenge by using low-rank matrices in the key-value (KV) layers, thereby allowing compressed latent KV states to be cached. Th... | {
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2502.07866 | Design and Implementation of Scalable Communication Interfaces for
Reliable and Stable Real-time Co-Simulation of Power Systems | [
"eess.SY",
"cs.SY"
] | Co-simulation offers an integrated approach for modeling the large-scale integration of inverter-based resources (IBRs) into transmission and distribution grids. This paper presents a scalable communication interface design and implementation to enable reliable and stable real-time co-simulation of power systems with h... | {
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2502.07869 | EventEgo3D++: 3D Human Motion Capture from a Head-Mounted Event Camera | [
"cs.CV"
] | Monocular egocentric 3D human motion capture remains a significant challenge, particularly under conditions of low lighting and fast movements, which are common in head-mounted device applications. Existing methods that rely on RGB cameras often fail under these conditions. To address these limitations, we introduce Ev... | {
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2502.07870 | TextAtlas5M: A Large-scale Dataset for Dense Text Image Generation | [
"cs.CV"
] | Text-conditioned image generation has gained significant attention in recent years and are processing increasingly longer and comprehensive text prompt. In everyday life, dense and intricate text appears in contexts like advertisements, infographics, and signage, where the integration of both text and visuals is essent... | {
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2502.07889 | A unifying account of warm start guarantees for patches of quantum
landscapes | [
"quant-ph",
"cs.LG",
"stat.ML"
] | Barren plateaus are fundamentally a statement about quantum loss landscapes on average but there can, and generally will, exist patches of barren plateau landscapes with substantial gradients. Previous work has studied certain classes of parameterized quantum circuits and found example regions where gradients vanish at... | {
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2502.07891 | The Observational Partial Order of Causal Structures with Latent
Variables | [
"stat.ML",
"cs.LG",
"quant-ph"
] | For two causal structures with the same set of visible variables, one is said to observationally dominate the other if the set of distributions over the visible variables realizable by the first contains the set of distributions over the visible variables realizable by the second. Knowing such dominance relations is us... | {
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2502.07904 | Intelligent Legal Assistant: An Interactive Clarification System for
Legal Question Answering | [
"cs.CL"
] | The rise of large language models has opened new avenues for users seeking legal advice. However, users often lack professional legal knowledge, which can lead to questions that omit critical information. This deficiency makes it challenging for traditional legal question-answering systems to accurately identify users'... | {
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2502.07905 | DeepSeek on a Trip: Inducing Targeted Visual Hallucinations via
Representation Vulnerabilities | [
"cs.CV",
"cs.LG"
] | Multimodal Large Language Models (MLLMs) represent the cutting edge of AI technology, with DeepSeek models emerging as a leading open-source alternative offering competitive performance to closed-source systems. While these models demonstrate remarkable capabilities, their vision-language integration mechanisms introdu... | {
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2502.07912 | Elevating Legal LLM Responses: Harnessing Trainable Logical Structures
and Semantic Knowledge with Legal Reasoning | [
"cs.CL"
] | Large Language Models (LLMs) have achieved impressive results across numerous domains, yet they experience notable deficiencies in legal question-answering tasks. LLMs often generate generalized responses that lack the logical specificity required for expert legal advice and are prone to hallucination, providing answer... | {
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2502.07922 | Visual-Haptic Model Mediated Teleoperation for Remote Ultrasound | [
"cs.RO",
"cs.HC"
] | Tele-ultrasound has the potential greatly to improve health equity for countless remote communities. However, practical scenarios involve potentially large time delays which cause current implementations of telerobotic ultrasound (US) to fail. Using a local model of the remote environment to provide haptics to the expe... | {
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2502.07923 | Sign Operator for Coping with Heavy-Tailed Noise: High Probability
Convergence Bounds with Extensions to Distributed Optimization and Comparison
Oracle | [
"math.OC",
"cs.LG"
] | The growing popularity of AI optimization problems involving severely corrupted data has increased the demand for methods capable of handling heavy-tailed noise, i.e., noise with bounded $\kappa$-th moment, $\kappa \in (1,2]$. For the widely used clipping technique, effectiveness heavily depends on the careful tuning o... | {
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2502.07924 | NDAI Agreements | [
"econ.TH",
"cs.AI"
] | We study a fundamental challenge in the economics of innovation: an inventor must reveal details of a new idea to secure compensation or funding, yet such disclosure risks expropriation. We present a model in which a seller (inventor) and buyer (investor) bargain over an information good under the threat of hold-up. In... | {
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2502.07931 | Educating a Responsible AI Workforce: Piloting a Curricular Module on AI
Policy in a Graduate Machine Learning Course | [
"cs.CY",
"cs.AI"
] | As artificial intelligence (AI) technologies begin to permeate diverse fields-from healthcare to education-consumers, researchers and policymakers are increasingly raising concerns about whether and how AI is regulated. It is therefore reasonable to anticipate that alignment with principles of 'ethical' or 'responsible... | {
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2502.07934 | Age of Information Optimization with Preemption Strategies for
Correlated Systems | [
"cs.IT",
"math.IT"
] | In this paper, we examine a multi-sensor system where each sensor monitors multiple dynamic information processes and transmits updates over a shared communication channel. These updates may include correlated information across the various processes. In this type of system, we analyze the impact of preemption, where o... | {
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2502.07937 | Active Advantage-Aligned Online Reinforcement Learning with Offline Data | [
"cs.LG",
"stat.ML"
] | Online reinforcement learning (RL) enhances policies through direct interactions with the environment, but faces challenges related to sample efficiency. In contrast, offline RL leverages extensive pre-collected data to learn policies, but often produces suboptimal results due to limited data coverage. Recent efforts h... | {
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2502.07938 | Adapting Multilingual Embedding Models to Historical Luxembourgish | [
"cs.CL"
] | The growing volume of digitized historical texts requires effective semantic search using text embeddings. However, pre-trained multilingual models, typically evaluated on contemporary texts, face challenges with historical digitized content due to OCR noise and outdated spellings. We explore the use of multilingual em... | {
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2502.07939 | Discrete Markov Probabilistic Models | [
"stat.ML",
"cs.LG"
] | This paper introduces the Discrete Markov Probabilistic Model (DMPM), a novel algorithm for discrete data generation. The algorithm operates in the space of bits $\{0,1\}^d$, where the noising process is a continuous-time Markov chain that can be sampled exactly via a Poissonian clock that flips labels uniformly at ran... | {
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2502.07942 | Symbiotic Cooperation for Web Agents: Harnessing Complementary Strengths
of Large and Small LLMs | [
"cs.MA",
"cs.LG"
] | Web browsing agents powered by large language models (LLMs) have shown tremendous potential in automating complex web-based tasks. Existing approaches typically rely on large LLMs (e.g., GPT-4o) to explore web environments and generate trajectory data, which is then used either for demonstration retrieval (for large LL... | {
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2502.07943 | CREDAL: Close Reading of Data Models | [
"cs.DB",
"cs.AI",
"cs.CY"
] | Data models are necessary for the birth of data and of any data-driven system. Indeed, every algorithm, every machine learning model, every statistical model, and every database has an underlying data model without which the system would not be usable. Hence, data models are excellent sites for interrogating the (mater... | {
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2502.07944 | SHACL-SKOS Based Knowledge Representation of Material Safety Data Sheet
(SDS) for the Pharmaceutical Industry | [
"cs.AI"
] | We report the development of a knowledge representation and reasoning (KRR) system built on hybrid SHACL-SKOS ontologies for globally harmonized system (GHS) material Safety Data Sheets (SDS) to enhance chemical safety communication and regulatory compliance. SDS are comprehensive documents containing safety and handli... | {
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2502.07945 | SurGrID: Controllable Surgical Simulation via Scene Graph to Image
Diffusion | [
"cs.CV",
"cs.LG"
] | Surgical simulation offers a promising addition to conventional surgical training. However, available simulation tools lack photorealism and rely on hardcoded behaviour. Denoising Diffusion Models are a promising alternative for high-fidelity image synthesis, but existing state-of-the-art conditioning methods fall shor... | {
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2502.07949 | VSC-RL: Advancing Autonomous Vision-Language Agents with Variational
Subgoal-Conditioned Reinforcement Learning | [
"cs.LG",
"cs.AI"
] | State-of-the-art (SOTA) reinforcement learning (RL) methods enable the vision-language agents to learn from interactions with the environment without human supervision. However, they struggle with learning inefficiencies in tackling real-world complex sequential decision-making tasks, especially with sparse reward sign... | {
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2502.07951 | Federated Self-supervised Domain Generalization for Label-efficient
Polyp Segmentation | [
"cs.CV",
"cs.DC",
"cs.LG"
] | Employing self-supervised learning (SSL) methodologies assumes par-amount significance in handling unlabeled polyp datasets when building deep learning-based automatic polyp segmentation models. However, the intricate privacy dynamics surrounding medical data often preclude seamless data sharing among disparate medical... | {
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2502.07957 | Intrinsic Bias is Predicted by Pretraining Data and Correlates with
Downstream Performance in Vision-Language Encoders | [
"cs.AI"
] | While recent work has found that vision-language models trained under the Contrastive Language Image Pre-training (CLIP) framework contain intrinsic social biases, the extent to which different upstream pre-training features of the framework relate to these biases, and hence how intrinsic bias and downstream performanc... | {
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2502.07962 | ESPFormer: Doubly-Stochastic Attention with Expected Sliced Transport
Plans | [
"cs.LG"
] | While self-attention has been instrumental in the success of Transformers, it can lead to over-concentration on a few tokens during training, resulting in suboptimal information flow. Enforcing doubly-stochastic constraints in attention matrices has been shown to improve structure and balance in attention distributions... | {
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2502.07963 | Caught in the Web of Words: Do LLMs Fall for Spin in Medical Literature? | [
"cs.CL",
"cs.AI"
] | Medical research faces well-documented challenges in translating novel treatments into clinical practice. Publishing incentives encourage researchers to present "positive" findings, even when empirical results are equivocal. Consequently, it is well-documented that authors often spin study results, especially in articl... | {
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2502.07964 | New tools for comparing classical and neural ODE models for tumor growth | [
"cs.LG",
"q-bio.QM"
] | A new computational tool TumorGrowth$.$jl for modeling tumor growth is introduced. The tool allows the comparison of standard textbook models, such as General Bertalanffy and Gompertz, with some newer models, including, for the first time, neural ODE models. As an application, we revisit a human meta-study of non-small... | {
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2502.07968 | Generative Risk Minimization for Out-of-Distribution Generalization on
Graphs | [
"cs.LG",
"cs.AI"
] | Out-of-distribution (OOD) generalization on graphs aims at dealing with scenarios where the test graph distribution differs from the training graph distributions. Compared to i.i.d. data like images, the OOD generalization problem on graph-structured data remains challenging due to the non-i.i.d. property and complex s... | {
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2502.07971 | ReTreever: Tree-based Coarse-to-Fine Representations for Retrieval | [
"cs.IR",
"cs.AI",
"cs.LG"
] | Document retrieval is a core component of question-answering systems, as it enables conditioning answer generation on new and large-scale corpora. While effective, the standard practice of encoding documents into high-dimensional embeddings for similarity search entails large memory and compute footprints, and also mak... | {
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2502.07972 | Training Sparse Mixture Of Experts Text Embedding Models | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Transformer-based text embedding models have improved their performance on benchmarks like MIRACL and BEIR by increasing their parameter counts. However, this scaling approach introduces significant deployment challenges, including increased inference latency and memory usage. These challenges are particularly severe i... | {
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2502.07974 | From Hazard Identification to Controller Design: Proactive and
LLM-Supported Safety Engineering for ML-Powered Systems | [
"cs.SE",
"cs.AI",
"cs.LG"
] | Machine learning (ML) components are increasingly integrated into software products, yet their complexity and inherent uncertainty often lead to unintended and hazardous consequences, both for individuals and society at large. Despite these risks, practitioners seldom adopt proactive approaches to anticipate and mitiga... | {
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2502.07975 | Sink equilibria and the attractors of learning in games | [
"cs.GT",
"cs.LG"
] | Characterizing the limit behavior -- that is, the attractors -- of learning dynamics is one of the most fundamental open questions in game theory. In recent work in this front, it was conjectured that the attractors of the replicator dynamic are in one-to-one correspondence with the sink equilibria of the game -- the s... | {
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2502.07977 | RESIST: Resilient Decentralized Learning Using Consensus Gradient
Descent | [
"cs.LG",
"math.OC",
"stat.ML"
] | Empirical risk minimization (ERM) is a cornerstone of modern machine learning (ML), supported by advances in optimization theory that ensure efficient solutions with provable algorithmic convergence rates, which measure the speed at which optimization algorithms approach a solution, and statistical learning rates, whic... | {
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2502.07978 | A Survey of In-Context Reinforcement Learning | [
"cs.LG"
] | Reinforcement learning (RL) agents typically optimize their policies by performing expensive backward passes to update their network parameters. However, some agents can solve new tasks without updating any parameters by simply conditioning on additional context such as their action-observation histories. This paper su... | {
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2502.07979 | Joint Modelling Histology and Molecular Markers for Cancer
Classification | [
"cs.CV"
] | Cancers are characterized by remarkable heterogeneity and diverse prognosis. Accurate cancer classification is essential for patient stratification and clinical decision-making. Although digital pathology has been advancing cancer diagnosis and prognosis, the paradigm in cancer pathology has shifted from purely relying... | {
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2502.07980 | CIRCUIT: A Benchmark for Circuit Interpretation and Reasoning
Capabilities of LLMs | [
"cs.LG",
"cs.AI"
] | The role of Large Language Models (LLMs) has not been extensively explored in analog circuit design, which could benefit from a reasoning-based approach that transcends traditional optimization techniques. In particular, despite their growing relevance, there are no benchmarks to assess LLMs' reasoning capability about... | {
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2502.07982 | Deep Semantic Graph Learning via LLM based Node Enhancement | [
"cs.AI"
] | Graph learning has attracted significant attention due to its widespread real-world applications. Current mainstream approaches rely on text node features and obtain initial node embeddings through shallow embedding learning using GNNs, which shows limitations in capturing deep textual semantics. Recent advances in Lar... | {
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2502.07985 | MetaSC: Test-Time Safety Specification Optimization for Language Models | [
"cs.CL",
"cs.AI"
] | We propose a novel dynamic safety framework that optimizes language model (LM) safety reasoning at inference time without modifying model weights. Building on recent advances in self-critique methods, our approach leverages a meta-critique mechanism that iteratively updates safety prompts-termed specifications-to drive... | {
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2502.07987 | Universal Adversarial Attack on Aligned Multimodal LLMs | [
"cs.AI"
] | We propose a universal adversarial attack on multimodal Large Language Models (LLMs) that leverages a single optimized image to override alignment safeguards across diverse queries and even multiple models. By backpropagating through the vision encoder and language head, we craft a synthetic image that forces the model... | {
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2502.07990 | Learning Effective Dynamics across Spatio-Temporal Scales of Complex
Flows | [
"cs.LG",
"physics.comp-ph",
"physics.flu-dyn"
] | Modeling and simulation of complex fluid flows with dynamics that span multiple spatio-temporal scales is a fundamental challenge in many scientific and engineering domains. Full-scale resolving simulations for systems such as highly turbulent flows are not feasible in the foreseeable future, and reduced-order models m... | {
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2502.07993 | What is a Sketch-and-Precondition Derivation for Low-Rank Approximation?
Inverse Power Error or Inverse Power Estimation? | [
"math.NA",
"cs.CC",
"cs.LG",
"cs.NA",
"stat.CO",
"stat.ML"
] | Randomized sketching accelerates large-scale numerical linear algebra by reducing computational complexity. While the traditional sketch-and-solve approach reduces the problem size directly through sketching, the sketch-and-precondition method leverages sketching to construct a computational friendly preconditioner. Th... | {
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2502.07998 | Adaptive kernel predictors from feature-learning infinite limits of
neural networks | [
"cs.LG",
"cond-mat.dis-nn",
"stat.ML"
] | Previous influential work showed that infinite width limits of neural networks in the lazy training regime are described by kernel machines. Here, we show that neural networks trained in the rich, feature learning infinite-width regime in two different settings are also described by kernel machines, but with data-depen... | {
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2502.08001 | Unveiling Client Privacy Leakage from Public Dataset Usage in Federated
Distillation | [
"cs.CR",
"cs.LG"
] | Federated Distillation (FD) has emerged as a popular federated training framework, enabling clients to collaboratively train models without sharing private data. Public Dataset-Assisted Federated Distillation (PDA-FD), which leverages public datasets for knowledge sharing, has become widely adopted. Although PDA-FD enh... | {
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2502.08003 | Heterogeneous Multi-agent Multi-armed Bandits on Stochastic Block Models | [
"cs.LG"
] | We study a novel heterogeneous multi-agent multi-armed bandit problem with a cluster structure induced by stochastic block models, influencing not only graph topology, but also reward heterogeneity. Specifically, agents are distributed on random graphs based on stochastic block models - a generalized Erdos-Renyi model ... | {
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2502.08004 | Optimizing Likelihoods via Mutual Information: Bridging Simulation-Based
Inference and Bayesian Optimal Experimental Design | [
"stat.ML",
"cs.LG"
] | Simulation-based inference (SBI) is a method to perform inference on a variety of complex scientific models with challenging inference (inverse) problems. Bayesian Optimal Experimental Design (BOED) aims to efficiently use experimental resources to make better inferences. Various stochastic gradient-based BOED methods ... | {
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2502.08005 | Towards Training One-Step Diffusion Models Without Distillation | [
"cs.LG",
"cs.CV"
] | Recent advances in one-step generative models typically follow a two-stage process: first training a teacher diffusion model and then distilling it into a one-step student model. This distillation process traditionally relies on both the teacher model's score function to compute the distillation loss and its weights fo... | {
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2502.08006 | Greed is Good: Guided Generation from a Greedy Perspective | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Training-free guided generation is a widely used and powerful technique that allows the end user to exert further control over the generative process of diffusion models. In this work, we explore the guided generation from the perspective of optimizing the solution trajectory of a neural differential equation in a gree... | {
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2502.08007 | The Role of Randomness in Stability | [
"cs.LG",
"stat.ML"
] | Stability is a central property in learning and statistics promising the output of an algorithm $A$ does not change substantially when applied to similar datasets $S$ and $S'$. It is an elementary fact that any sufficiently stable algorithm (e.g.\ one returning the same result with high probability, satisfying privacy ... | {
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2502.08008 | An Interactive Framework for Implementing Privacy-Preserving Federated
Learning: Experiments on Large Language Models | [
"cs.LG",
"cs.CR"
] | Federated learning (FL) enhances privacy by keeping user data on local devices. However, emerging attacks have demonstrated that the updates shared by users during training can reveal significant information about their data. This has greatly thwart the adoption of FL methods for training robust AI models in sensitive ... | {
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2502.08009 | The Geometry of Prompting: Unveiling Distinct Mechanisms of Task
Adaptation in Language Models | [
"cs.CL"
] | Decoder-only language models have the ability to dynamically switch between various computational tasks based on input prompts. Despite many successful applications of prompting, there is very limited understanding of the internal mechanism behind such flexibility. In this work, we investigate how different prompting m... | {
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2502.08011 | Training-Free Safe Denoisers for Safe Use of Diffusion Models | [
"cs.AI"
] | There is growing concern over the safety of powerful diffusion models (DMs), as they are often misused to produce inappropriate, not-safe-for-work (NSFW) content or generate copyrighted material or data of individuals who wish to be forgotten. Many existing methods tackle these issues by heavily relying on text-based n... | {
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2502.08020 | Speculate, then Collaborate: Fusing Knowledge of Language Models during
Decoding | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) often excel in specific domains but fall short in others due to the limitations of their training. Thus, enabling LLMs to solve problems collaboratively by integrating their complementary knowledge promises to improve their performance across domains. To realize this potential, we introduce... | {
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2502.08021 | Model Selection for Off-policy Evaluation: New Algorithms and
Experimental Protocol | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Holdout validation and hyperparameter tuning from data is a long-standing problem in offline reinforcement learning (RL). A standard framework is to use off-policy evaluation (OPE) methods to evaluate and select the policies, but OPE either incurs exponential variance (e.g., importance sampling) or has hyperparameters ... | {
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2502.08023 | Performance Analysis of Infrastructure Sharing Techniques in Cellular
Networks: A Percolation Theory Approach | [
"eess.SY",
"cs.SY"
] | In the context of 5G, infrastructure sharing has been identified as a potential solution to reduce the investment costs of cellular networks. In particular, it can help low-income regions build 5G networks more affordably and further bridge the digital divide. There are two main kinds of infrastructure sharing: passive... | {
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2502.08024 | Initialization Matters: Unraveling the Impact of Pre-Training on
Federated Learning | [
"cs.LG",
"cs.DC"
] | Initializing with pre-trained models when learning on downstream tasks is becoming standard practice in machine learning. Several recent works explore the benefits of pre-trained initialization in a federated learning (FL) setting, where the downstream training is performed at the edge clients with heterogeneous data d... | {
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2502.08025 | From Brainwaves to Brain Scans: A Robust Neural Network for EEG-to-fMRI
Synthesis | [
"cs.CV"
] | While functional magnetic resonance imaging (fMRI) offers rich spatial resolution, it is limited by high operational costs and significant infrastructural demands. In contrast, electroencephalography (EEG) provides millisecond-level precision in capturing electrical activity but lacks the spatial resolution necessary f... | {
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2502.08026 | Contextual Subspace Manifold Projection for Structural Refinement of
Large Language Model Representations | [
"cs.CL"
] | Internal representations within deep neural architectures encode high-dimensional abstractions of linguistic structures, yet they often exhibit inefficiencies in feature distribution, limiting expressiveness and adaptability. Contextual Subspace Manifold Projection introduces a structured refinement technique that sele... | {
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2502.08033 | End-to-End Predictive Planner for Autonomous Driving with Consistency
Models | [
"cs.RO",
"cs.LG"
] | Trajectory prediction and planning are fundamental components for autonomous vehicles to navigate safely and efficiently in dynamic environments. Traditionally, these components have often been treated as separate modules, limiting the ability to perform interactive planning and leading to computational inefficiency in... | {
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2502.08037 | Franken-Adapter: Cross-Lingual Adaptation of LLMs by Embedding Surgery | [
"cs.CL"
] | The capabilities of Large Language Models (LLMs) in low-resource languages lag far behind those in English, making their universal accessibility a significant challenge. To alleviate this, we present $\textit{Franken-Adapter}$, a modular language adaptation approach for decoder-only LLMs with embedding surgery. Our met... | {
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2502.08041 | The Art of Misclassification: Too Many Classes, Not Enough Points | [
"cs.LG",
"cs.IT",
"math.IT"
] | Classification is a ubiquitous and fundamental problem in artificial intelligence and machine learning, with extensive efforts dedicated to developing more powerful classifiers and larger datasets. However, the classification task is ultimately constrained by the intrinsic properties of datasets, independently of compu... | {
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2502.08045 | Break the Checkbox: Challenging Closed-Style Evaluations of Cultural
Alignment in LLMs | [
"cs.CL",
"cs.AI",
"cs.CY"
] | A large number of studies rely on closed-style multiple-choice surveys to evaluate cultural alignment in Large Language Models (LLMs). In this work, we challenge this constrained evaluation paradigm and explore more realistic, unconstrained approaches. Using the World Values Survey (WVS) and Hofstede Cultural Dimension... | {
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2502.08047 | WorldGUI: Dynamic Testing for Comprehensive Desktop GUI Automation | [
"cs.AI",
"cs.MA"
] | Current GUI agents have achieved outstanding performance in GUI element grounding. However, planning remains highly challenging, especially due to sensitivity to the initial state of the environment. Specifically, slight differences in the initial state-such as the target software not being open or the interface not be... | {
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2502.08054 | COMBO-Grasp: Learning Constraint-Based Manipulation for Bimanual
Occluded Grasping | [
"cs.RO",
"cs.LG"
] | This paper addresses the challenge of occluded robot grasping, i.e. grasping in situations where the desired grasp poses are kinematically infeasible due to environmental constraints such as surface collisions. Traditional robot manipulation approaches struggle with the complexity of non-prehensile or bimanual strategi... | {
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2502.08055 | SLVR: Securely Leveraging Client Validation for Robust Federated
Learning | [
"cs.CR",
"cs.LG"
] | Federated Learning (FL) enables collaborative model training while keeping client data private. However, exposing individual client updates makes FL vulnerable to reconstruction attacks. Secure aggregation mitigates such privacy risks but prevents the server from verifying the validity of each client update, creating a... | {
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2502.08056 | Cognify: Supercharging Gen-AI Workflows With Hierarchical Autotuning | [
"cs.LG",
"cs.AI",
"cs.MA"
] | Today's gen-AI workflows that involve multiple ML model calls, tool/API calls, data retrieval, or generic code execution are often tuned manually in an ad-hoc way that is both time-consuming and error-prone. In this paper, we propose a systematic approach for automatically tuning gen-AI workflows. Our key insight is th... | {
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2502.08058 | General Coded Computing: Adversarial Settings | [
"cs.DC",
"cs.LG"
] | Conventional coded computing frameworks are predominantly tailored for structured computations, such as matrix multiplication and polynomial evaluation. Such tasks allow the reuse of tools and techniques from algebraic coding theory to improve the reliability of distributed systems in the presence of stragglers and adv... | {
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2502.08059 | On Mechanistic Circuits for Extractive Question-Answering | [
"cs.CL",
"cs.LG"
] | Large language models are increasingly used to process documents and facilitate question-answering on them. In our paper, we extract mechanistic circuits for this real-world language modeling task: context-augmented language modeling for extractive question-answering (QA) tasks and understand the potential benefits of ... | {
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2502.08063 | Multi-Agent Performative Prediction Beyond the Insensitivity Assumption:
A Case Study for Mortgage Competition | [
"cs.GT",
"cs.LG"
] | Performative prediction models account for feedback loops in decision-making processes where predictions influence future data distributions. While existing work largely assumes insensitivity of data distributions to small strategy changes, this assumption usually fails in real-world competitive (i.e. multi-agent) sett... | {
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2502.08071 | Collaborative Filtering Meets Spectrum Shift: Connecting User-Item
Interaction with Graph-Structured Side Information | [
"cs.IR"
] | Graph Neural Network (GNN) has demonstrated their superiority in collaborative filtering, where the user-item (U-I) interaction bipartite graph serves as the fundamental data format. However, when graph-structured side information (e.g., multimodal similarity graphs or social networks) is integrated into the U-I bipart... | {
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2502.08075 | Knowledge Swapping via Learning and Unlearning | [
"cs.CV"
] | We introduce \textbf{Knowledge Swapping}, a novel task designed to selectively regulate knowledge of a pretrained model by enabling the forgetting of user\-specified information, retaining essential knowledge, and acquiring new knowledge simultaneously. By delving into the analysis of knock-on feature hierarchy, we fin... | {
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2502.08077 | Cascading Bandits Robust to Adversarial Corruptions | [
"cs.LG"
] | Online learning to rank sequentially recommends a small list of items to users from a large candidate set and receives the users' click feedback. In many real-world scenarios, users browse the recommended list in order and click the first attractive item without checking the rest. Such behaviors are usually formulated ... | {
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2502.08079 | MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained
Models | [
"cs.CV"
] | Current adversarial attacks for evaluating the robustness of vision-language pre-trained (VLP) models in multi-modal tasks suffer from limited transferability, where attacks crafted for a specific model often struggle to generalize effectively across different models, limiting their utility in assessing robustness more... | {
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2502.08080 | NLI under the Microscope: What Atomic Hypothesis Decomposition Reveals | [
"cs.CL"
] | Decomposition of text into atomic propositions is a flexible framework allowing for the closer inspection of input and output text. We use atomic decomposition of hypotheses in two natural language reasoning tasks, traditional NLI and defeasible NLI, to form atomic sub-problems, or granular inferences that models must ... | {
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2502.08083 | Mixture of Decoupled Message Passing Experts with Entropy Constraint for
General Node Classification | [
"cs.LG",
"cs.SI"
] | The varying degrees of homophily and heterophily in real-world graphs persistently constrain the universality of graph neural networks (GNNs) for node classification. Adopting a data-centric perspective, this work reveals an inherent preference of different graphs towards distinct message encoding schemes: homophilous ... | {
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2502.08089 | A Cooperative Bearing-Rate Approach for Observability-Enhanced Target
Motion Estimation | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Vision-based target motion estimation is a fundamental problem in many robotic tasks. The existing methods have the limitation of low observability and, hence, face challenges in tracking highly maneuverable targets. Motivated by the aerial target pursuit task where a target may maneuver in 3D space, this paper studies... | {
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2502.08092 | GCoT: Chain-of-Thought Prompt Learning for Graphs | [
"cs.CL",
"cs.AI"
] | Chain-of-thought (CoT) prompting has achieved remarkable success in natural language processing (NLP). However, its vast potential remains largely unexplored for graphs. This raises an interesting question: How can we design CoT prompting for graphs to guide graph models to learn step by step? On one hand, unlike natur... | {
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2502.08093 | Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity
Integration using Gaussian Process | [
"cs.RO"
] | Radar ensures robust sensing capabilities in adverse weather conditions, yet challenges remain due to its high inherent noise level. Existing radar odometry has overcome these challenges with strategies such as filtering spurious points, exploiting Doppler velocity, or integrating with inertial measurements. This paper... | {
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2502.08097 | ID-Cloak: Crafting Identity-Specific Cloaks Against Personalized
Text-to-Image Generation | [
"cs.CV",
"cs.CR"
] | Personalized text-to-image models allow users to generate images of new concepts from several reference photos, thereby leading to critical concerns regarding civil privacy. Although several anti-personalization techniques have been developed, these methods typically assume that defenders can afford to design a privacy... | {
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2502.08098 | Unsupervised categorization of similarity measures | [
"cs.LG",
"cs.NE"
] | In general, objects can be distinguished on the basis of their features, such as color or shape. In particular, it is assumed that similarity judgments about such features can be processed independently in different metric spaces. However, the unsupervised categorization mechanism of metric spaces corresponding to obje... | {
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2502.08101 | Rethinking Tokenized Graph Transformers for Node Classification | [
"cs.LG",
"cs.AI"
] | Node tokenized graph Transformers (GTs) have shown promising performance in node classification. The generation of token sequences is the key module in existing tokenized GTs which transforms the input graph into token sequences, facilitating the node representation learning via Transformer. In this paper, we observe t... | {
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} |
2502.08105 | Out-of-Distribution Detection on Graphs: A Survey | [
"cs.LG"
] | Graph machine learning has witnessed rapid growth, driving advancements across diverse domains. However, the in-distribution assumption, where training and testing data share the same distribution, often breaks in real-world scenarios, leading to degraded model performance under distribution shifts. This challenge has ... | {
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} |
2502.08106 | PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced
Text-to-Image Generation | [
"cs.LG",
"cs.AI",
"cs.CV",
"stat.ML"
] | Diffusion models have made significant advancements in recent years. However, their performance often deteriorates when trained or fine-tuned on imbalanced datasets. This degradation is largely due to the disproportionate representation of majority and minority data in image-text pairs. In this paper, we propose a gene... | {
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} |
2502.08108 | Generative AI and Empirical Software Engineering: A Paradigm Shift | [
"cs.SE",
"cs.AI"
] | The widespread adoption of generative AI in software engineering marks a paradigm shift, offering new opportunities to design and utilize software engineering tools while influencing both developers and the artifacts they create. Traditional empirical methods in software engineering, including quantitative, qualitative... | {
"Other": 1,
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} |
2502.08109 | HuDEx: Integrating Hallucination Detection and Explainability for
Enhancing the Reliability of LLM responses | [
"cs.CL",
"cs.AI"
] | Recent advances in large language models (LLMs) have shown promising improvements, often surpassing existing methods across a wide range of downstream tasks in natural language processing. However, these models still face challenges, which may hinder their practical applicability. For example, the phenomenon of halluci... | {
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} |
2502.08115 | Neuromorphic Digital-Twin-based Controller for Indoor Multi-UAV Systems
Deployment | [
"cs.NE"
] | Presented study introduces a novel distributed cloud-edge framework for autonomous multi-UAV systems that combines the computational efficiency of neuromorphic computing with nature-inspired control strategies. The proposed architecture equips each UAV with an individual Spiking Neural Network (SNN) that learns to repr... | {
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} |
2502.08119 | Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for
Unmanned Surface Vehicles | [
"cs.AI",
"cs.RO"
] | The increasing deployment of unmanned surface vehicles (USVs) require computational support and coverage in applications such as maritime search and rescue. Unmanned aerial vehicles (UAVs) can offer low-cost, flexible aerial services, and ground stations (GSs) can provide powerful supports, which can cooperate to help ... | {
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"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
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"cs.SY": 0
} |
2502.08122 | Hookpad Aria: A Copilot for Songwriters | [
"cs.SD",
"cs.AI",
"cs.LG"
] | We present Hookpad Aria, a generative AI system designed to assist musicians in writing Western pop songs. Our system is seamlessly integrated into Hookpad, a web-based editor designed for the composition of lead sheets: symbolic music scores that describe melody and harmony. Hookpad Aria has numerous generation capabi... | {
"Other": 0,
"cs.AI": 1,
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} |
2502.08123 | Provably Robust Federated Reinforcement Learning | [
"cs.CR",
"cs.DC",
"cs.LG"
] | Federated reinforcement learning (FRL) allows agents to jointly learn a global decision-making policy under the guidance of a central server. While FRL has advantages, its decentralized design makes it prone to poisoning attacks. To mitigate this, Byzantine-robust aggregation techniques tailored for FRL have been intro... | {
"Other": 1,
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} |
2502.08125 | Incremental Approximate Single-Source Shortest Paths with Predictions | [
"cs.DS",
"cs.LG"
] | The algorithms-with-predictions framework has been used extensively to develop online algorithms with improved beyond-worst-case competitive ratios. Recently, there is growing interest in leveraging predictions for designing data structures with improved beyond-worst-case running times. In this paper, we study the fund... | {
"Other": 1,
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"cs.SD": 0,
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"cs.SY": 0
} |
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