Most Cited ICML "breathing monitoring" Papers
5,975 papers found • Page 25 of 30
Conference
High-Dimensional Geometric Streaming for Nearly Low Rank Data
Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni et al.
PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses
Adel Javanmard, Matthew Fahrbach, Vahab Mirrokni
Perturb-and-Project: Differentially Private Similarities and Marginals
Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto et al.
Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond
Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger et al.
A Field Guide for Pacing Budget and ROS Constraints
Santiago Balseiro, Kshipra Bhawalkar, Zhe Feng et al.
PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control
Ruijie Zheng, Ching-An Cheng, Hal Daumé et al.
Deep Networks Always Grok and Here is Why
Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk
Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint
Wei Xiong, Hanze Dong, Chenlu Ye et al.
Contrastive Predict-and-Search for Mixed Integer Linear Programs
Taoan Huang, Aaron Ferber, Arman Zharmagambetov et al.
Learning Decision Trees and Forests with Algorithmic Recourse
Kentaro Kanamori, Takuya Takagi, Ken Kobayashi et al.
Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures
Zenan Ling, Longbo Li, Zhanbo Feng et al.
AMPA: Adaptive Mixed Precision Allocation for Low-Bit Integer Training
Li Ding, Wen Fei, Yuyang Huang et al.
MusicFlow: Cascaded Flow Matching for Text Guided Music Generation
Prajwal K R, Bowen Shi, Matthew Le et al.
Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients
Mengmeng Ma, Tang Li, Xi Peng
Bayesian Design Principles for Offline-to-Online Reinforcement Learning
Hao Hu, yiqin yang, Jianing Ye et al.
Differentiable Distributionally Robust Optimization Layers
Xutao Ma, Chao Ning, WenLi Du
Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks
Zhuomin Chen, Jiaxing Zhang, Jingchao Ni et al.
TimeX++: Learning Time-Series Explanations with Information Bottleneck
Zichuan Liu, Tianchun Wang, Jimeng Shi et al.
Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs
Andries Smit, Nathan Grinsztajn, Paul Duckworth et al.
NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models
Zeqian Ju, Yuancheng Wang, Kai Shen et al.
Position: Near to Mid-term Risks and Opportunities of Open-Source Generative AI
Francisco Eiras, Aleksandar Petrov, Bertie Vidgen et al.
Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study
Shusheng Xu, Wei Fu, Jiaxuan Gao et al.
Open-Domain Text Evaluation via Contrastive Distribution Methods
Sidi Lu, Hongyi Liu, Asli Celikyilmaz et al.
DiNADO: Norm-Disentangled Neurally-Decomposed Oracles for Controlling Language Models
Sidi Lu, Wenbo Zhao, Chenyang Tao et al.
Listening to the noise: Blind Denoising with Gibbs Diffusion
David Heurtel-Depeiges, Charles Margossian, Ruben Ohana et al.
GATE: How to Keep Out Intrusive Neighbors
Nimrah Mustafa, Rebekka Burkholz
End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations
Lirui Luo, Guoxi Zhang, Hongming Xu et al.
Larimar: Large Language Models with Episodic Memory Control
Payel Das, Subhajit Chaudhury, Elliot Nelson et al.
What Would Gauss Say About Representations? Probing Pretrained Image Models using Synthetic Gaussian Benchmarks
Ching-Yun (Irene) Ko, Pin-Yu Chen, Payel Das et al.
Position: Data-driven Discovery with Large Generative Models
Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal et al.
Matrix Information Theory for Self-Supervised Learning
Yifan Zhang, Zhiquan Tan, Jingqin Yang et al.
Information Flow in Self-Supervised Learning
Zhiquan Tan, Jingqin Yang, Weiran Huang et al.
Better & Faster Large Language Models via Multi-token Prediction
Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Roziere et al.
ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data
Carmen Martin-Turrero, Maxence Bouvier, Manuel Breitenstein et al.
CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks
Shashank Agnihotri, Steffen Jung, Margret Keuper
Promptbreeder: Self-Referential Self-Improvement via Prompt Evolution
Chrisantha Fernando, Dylan Banarse, Henryk Michalewski et al.
Position: Open-Endedness is Essential for Artificial Superhuman Intelligence
Edward Hughes, Michael Dennis, Jack Parker-Holder et al.
Debating with More Persuasive LLMs Leads to More Truthful Answers
Akbir Khan, John Hughes, Dan Valentine et al.
Genie: Generative Interactive Environments
Jake Bruce, Michael Dennis, Ashley Edwards et al.
HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
Andrey Bryutkin, Jiahao Huang, Zhongying Deng et al.
ATraDiff: Accelerating Online Reinforcement Learning with Imaginary Trajectories
Qianlan Yang, Yu-Xiong Wang
Data-Efficient Molecular Generation with Hierarchical Textual Inversion
Seojin Kim, Jaehyun Nam, Sihyun Yu et al.
All-in-one simulation-based inference
Manuel Gloeckler, Michael Deistler, Christian Weilbach et al.
Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual Features
Thalles Silva, Helio Pedrini, Adín Ramírez Rivera
Position: A Safe Harbor for AI Evaluation and Red Teaming
Shayne Longpre, Sayash Kapoor, Kevin Klyman et al.
Evaluation of Trajectory Distribution Predictions with Energy Score
Novin Shahroudi, Mihkel Lepson, Meelis Kull
Position: Future Directions in the Theory of Graph Machine Learning
Christopher Morris, Fabrizio Frasca, Nadav Dym et al.
Hierarchical Integral Probability Metrics: A distance on random probability measures with low sample complexity
Marta Catalano, Hugo Lavenant
HexGen: Generative Inference of Large Language Model over Heterogeneous Environment
Youhe Jiang, Ran Yan, Xiaozhe Yao et al.
Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience
Martina G. Vilas, Federico Adolfi, David Poeppel et al.
Implicit Bias of Policy Gradient in Linear Quadratic Control: Extrapolation to Unseen Initial States
Noam Razin, Yotam Alexander, Edo Cohen-Karlik et al.
Stochastic positional embeddings improve masked image modeling
Amir Bar, Florian Bordes, Assaf Shocher et al.
Differentially Private Representation Learning via Image Captioning
Tom Sander, Yaodong Yu, Maziar Sanjabi et al.
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
Shengzhuang Chen, Jihoon Tack, Yunqiao Yang et al.
Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues
Antonio Orvieto, Soham De, Caglar Gulcehre et al.
PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-Performer
Chang Chen, Junyeob Baek, Fei Deng et al.
On Gradient-like Explanation under a Black-box Setting: When Black-box Explanations Become as Good as White-box
Yi Cai, Gerhard Wunder
Tabular Insights, Visual Impacts: Transferring Expertise from Tables to Images
Jun-Peng Jiang, Han-Jia Ye, Leye Wang et al.
Neural operators meet conjugate gradients: The FCG-NO method for efficient PDE solving
Alexander Rudikov, Fanaskov Vladimir, Ekaterina Muravleva et al.
No Double Descent in Principal Component Regression: A High-Dimensional Analysis
Daniel Gedon, Antonio Ribeiro, Thomas Schön
Dynamic Facility Location in High Dimensional Euclidean Spaces
Sayan Bhattacharya, Gramoz Goranci, Shaofeng Jiang et al.
Defense against Backdoor Attack on Pre-trained Language Models via Head Pruning and Attention Normalization
Xingyi Zhao, Depeng Xu, Shuhan Yuan
Rethinking the Flat Minima Searching in Federated Learning
Taehwan Lee, Sung Whan Yoon
Bounded and Uniform Energy-based Out-of-distribution Detection for Graphs
Shenzhi Yang, Bin Liang, An Liu et al.
Deconstructing the Goldilocks Zone of Neural Network Initialization
Artem Vysogorets, Anna Dawid, Julia Kempe
AutoOS: Make Your OS More Powerful by Exploiting Large Language Models
Huilai Chen, Yuanbo Wen, Limin Cheng et al.
Gradient-based Visual Explanation for Transformer-based CLIP
Chenyang ZHAO, Kun Wang, Xingyu Zeng et al.
Performance Bounds for Active Binary Testing with Information Maximization
Aditya Chattopadhyay, Benjamin Haeffele, Rene Vidal et al.
Latent Noise Segmentation: How Neural Noise Leads to the Emergence of Segmentation and Grouping
Ben Lonnqvist, Zhengqing Wu, Michael Herzog
Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective
Yajie Bao, Michael Crawshaw, Mingrui Liu
Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise
Thomas Pouplin, Alan Jeffares, Nabeel Seedat et al.
A Fixed-Point Approach for Causal Generative Modeling
Meyer Scetbon, Joel Jennings, Agrin Hilmkil et al.
Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention
Jiaqi Zhang, Joel Jennings, Agrin Hilmkil et al.
Conditional Language Learning with Context
Xiao Zhang, Miao Li, Ji Wu
Getting the most out of your tokenizer for pre-training and domain adaptation
Gautier Dagan, Gabriel Synnaeve, Baptiste Roziere
Optimization without Retraction on the Random Generalized Stiefel Manifold
Simon Vary, Pierre Ablin, Bin Gao et al.
Representing Molecules as Random Walks Over Interpretable Grammars
Michael Sun, Minghao Guo, Weize Yuan et al.
Towards a Better Theoretical Understanding of Independent Subnetwork Training
Egor Shulgin, Peter Richtarik
AegisFL: Efficient and Flexible Privacy-Preserving Byzantine-Robust Cross-silo Federated Learning
Dong Chen, Hongyuan Qu, Guangwu Xu
One for All: A Universal Generator for Concept Unlearnability via Multi-Modal Alignment
Chaochao Chen, Jiaming Zhang, Yuyuan Li et al.
Forget Sharpness: Perturbed Forgetting of Model Biases Within SAM Dynamics
Ankit Vani, Frederick Tung, Gabriel Oliveira et al.
Just Cluster It: An Approach for Exploration in High-Dimensions using Clustering and Pre-Trained Representations
Stefan Sylvius Wagner Martinez, Stefan Harmeling
Risk Aware Benchmarking of Large Language Models
Apoorva Nitsure, Youssef Mroueh, Mattia Rigotti et al.
Deeper or Wider: A Perspective from Optimal Generalization Error with Sobolev Loss
Yahong Yang, Juncai He
Towards a Self-contained Data-driven Global Weather Forecasting Framework
Yi Xiao, LEI BAI, Wei Xue et al.
Two-timescale Derivative Free Optimization for Performative Prediction with Markovian Data
Haitong LIU, Qiang Li, Hoi To Wai
To Each (Textual Sequence) Its Own: Improving Memorized-Data Unlearning in Large Language Models
George-Octavian Bărbulescu, Peter Triantafillou
S$\Omega$I: Score-based O-INFORMATION Estimation
Mustapha BOUNOUA, Giulio Franzese, Pietro Michiardi
SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks
Jiwon Song, Kyungseok Oh, Taesu Kim et al.
DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning
Ashwin Hebbar, Sravan Kumar Ankireddy, Hyeji Kim et al.
Two Heads are Actually Better than One: Towards Better Adversarial Robustness via Transduction and Rejection
Nils Palumbo, Yang Guo, Xi Wu et al.
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control
Amrit Singh Bedi, Anjaly Parayil, Junyu Zhang et al.
Position: On the Possibilities of AI-Generated Text Detection
Souradip Chakraborty, Amrit Singh Bedi, Sicheng Zhu et al.
PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling
Utsav Singh, Wesley A. Suttle, Brian Sadler et al.
MaxMin-RLHF: Alignment with Diverse Human Preferences
Souradip Chakraborty, Jiahao Qiu, Hui Yuan et al.
Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles
Bhrij Patel, Wesley A. Suttle, Alec Koppel et al.
Don't be so Negative! Score-based Generative Modeling with Oracle-assisted Guidance
Saeid Naderiparizi, Xiaoxuan Liang, Setareh Cohan et al.
Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples
Andrew C. Cullen, Shijie Liu, Paul Montague et al.
Quantum Theory and Application of Contextual Optimal Transport
Nicola Mariella, Albert Akhriev, Francesco Tacchino et al.
Few-Shot Unsupervised Implicit Neural Shape Representation Learning with Spatial Adversaries
Amine Ouasfi, Adnane Boukhayma
Learning with Partial-Label and Unlabeled Data: A Uniform Treatment for Supervision Redundancy and Insufficiency
Yangfan Liu, JIAQI LYU, Xin Geng et al.
HyperFields: Towards Zero-Shot Generation of NeRFs from Text
Sudarshan Babu, Richard Liu, Zi Yu Zhou et al.
Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples
Thomas T. Zhang, Bruce Lee, Ingvar Ziemann et al.
AlphaZero-Like Tree-Search can Guide Large Language Model Decoding and Training
Ziyu Wan, Xidong Feng, Muning Wen et al.
Active Ranking and Matchmaking, with Perfect Matchings
Hafedh El Ferchichi, Matthieu LERASLE, Vianney Perchet
Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals
Ziyi Liu, Idan Attias, Daniel Roy
Towards Neural Architecture Search through Hierarchical Generative Modeling
Lichuan Xiang, Łukasz Dudziak, Mohamed Abdelfattah et al.
Learning from Students: Applying t-Distributions to Explore Accurate and Efficient Formats for LLMs
Jordan Dotzel, Yuzong Chen, Bahaa Kotb et al.
Amortized Variational Deep Kernel Learning
Alan Matias, César Lincoln Mattos, Joao Paulo Gomes et al.
Disentanglement Learning via Topology
Nikita Balabin, Daria Voronkova, Ilya Trofimov et al.
SaVeR: Optimal Data Collection Strategy for Safe Policy Evaluation in Tabular MDP
Subhojyoti Mukherjee, Josiah Hanna, Robert Nowak
Codebook Features: Sparse and Discrete Interpretability for Neural Networks
Alex Tamkin, Mohammad Taufeeque, Noah Goodman
Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics
Luca Grillotti, Maxence Faldor, Borja G. León et al.
Position: Embracing Negative Results in Machine Learning
Florian Karl, Malte Kemeter, Gabriel Dax et al.
Position: Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination
Zhiyao Luo, Yangchen Pan, Peter Watkinson et al.
Learning Divergence Fields for Shift-Robust Graph Representations
Qitian Wu, Fan Nie, Chenxiao Yang et al.
How Graph Neural Networks Learn: Lessons from Training Dynamics
Chenxiao Yang, Qitian Wu, David Wipf et al.
Graph Out-of-Distribution Detection Goes Neighborhood Shaping
Tianyi Bao, Qitian Wu, Zetian Jiang et al.
Stay on Topic with Classifier-Free Guidance
Guillaume Sanchez, Alexander Spangher, Honglu Fan et al.
Position: On the Societal Impact of Open Foundation Models
Sayash Kapoor, Rishi Bommasani, Kevin Klyman et al.
Learning Label Shift Correction for Test-Agnostic Long-Tailed Recognition
Tong Wei, Zhen Mao, Zi-Hao Zhou et al.
On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data
Shunxing Fan, Mingming Gong, Kun Zhang
Evaluation of Test-Time Adaptation Under Computational Time Constraints
Motasem Alfarra, Hani Itani, Alejandro Pardo et al.
Towards Interpretable Deep Local Learning with Successive Gradient Reconciliation
Yibo Yang, Xiaojie Li, Motasem Alfarra et al.
An Empirical Study of Realized GNN Expressiveness
Yanbo Wang, Muhan Zhang
Self-Driven Entropy Aggregation for Byzantine-Robust Heterogeneous Federated Learning
Wenke Huang, Zekun Shi, Mang Ye et al.
Understanding MLP-Mixer as a wide and sparse MLP
Tomohiro Hayase, Ryo Karakida
Self-attention Networks Localize When QK-eigenspectrum Concentrates
Han Bao, Ryuichiro Hataya, Ryo Karakida
Faster Streaming and Scalable Algorithms for Finding Directed Dense Subgraphs in Large Graphs
Slobodan Mitrovic, Theodore Pan
Sparse-IFT: Sparse Iso-FLOP Transformations for Maximizing Training Efficiency
Vithursan Thangarasa, Shreyas Saxena, Abhay Gupta et al.
Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks
Khurram Javed, Haseeb Shah, Richard Sutton et al.
Effect-Invariant Mechanisms for Policy Generalization
Sorawit Saengkyongam, Niklas Pfister, Predag Klasnja et al.
eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data
Peng, Xinyi Ling, Ziru Chen et al.
Differentiable Combinatorial Scheduling at Scale
Mingju Liu, Yingjie Li, Jiaqi Yin et al.
Bottleneck-Minimal Indexing for Generative Document Retrieval
Xin Du, Lixin Xiu, Kumiko Tanaka-Ishii
Model-based Reinforcement Learning for Parameterized Action Spaces
Renhao Zhang, Haotian Fu, Yilin Miao et al.
Efficient Mixture Learning in Black-Box Variational Inference
Alexandra Hotti, Oskar Kviman, Ricky Molén et al.
Indirectly Parameterized Concrete Autoencoders
Alfred Nilsson, Klas Wijk, Sai bharath chandra Gutha et al.
EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal Tokens
Sunil Hwang, Jaehong Yoon, Youngwan Lee et al.
STELLA: Continual Audio-Video Pre-training with SpatioTemporal Localized Alignment
Jaewoo Lee, Jaehong Yoon, Wonjae Kim et al.
BECoTTA: Input-dependent Online Blending of Experts for Continual Test-time Adaptation
Daeun Lee, Jaehong Yoon, Sung Ju Hwang
Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation
Danny Halawi, Alexander Wei, Eric Wallace et al.
What is Dataset Distillation Learning?
William Yang, Ye Zhu, Zhiwei Deng et al.
DPZero: Private Fine-Tuning of Language Models without Backpropagation
Liang Zhang, Bingcong Li, Kiran Thekumparampil et al.
MLI Formula: A Nearly Scale-Invariant Solution with Noise Perturbation
Bowen Tao, Xin-Chun Li, De-Chuan Zhan
Sampling in Unit Time with Kernel Fisher-Rao Flow
Aimee Maurais, Youssef Marzouk
Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks
Duy Nguyen, Nina Lukashina, Tai Nguyen et al.
Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity
Lu Yin, You Wu, Zhenyu Zhang et al.
Quantum Positional Encodings for Graph Neural Networks
Slimane Thabet, Mehdi Djellabi, Igor Sokolov et al.
Hidden Traveling Waves bind Working Memory Variables in Recurrent Neural Networks
Arjun Karuvally, Terrence Sejnowski, Hava Siegelmann
Disentangled 3D Scene Generation with Layout Learning
Dave Epstein, Ben Poole, Ben Mildenhall et al.
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices
Jiin Woo, Laixi Shi, Gauri Joshi et al.
Improved Dimensionality Dependence for Zeroth-Order Optimisation over Cross-Polytopes
Weijia Shao
A New Computationally Efficient Algorithm to solve Feature Selection for Functional Data Classification in High-dimensional Spaces
Tobia Boschi, FRANCESCA BONIN, Rodrigo Ordonez-Hurtado et al.
A Sparsity Principle for Partially Observable Causal Representation Learning
Danru Xu, Dingling Yao, Sébastien Lachapelle et al.
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations
Henrik Schopmans, Pascal Friederich
Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization
Hao Wang, Kaifeng Yang, Michael Affenzeller
Position: Opportunities Exist for Machine Learning in Magnetic Fusion Energy
Lucas Spangher, Allen Wang, Andrew Maris et al.
Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data
Yvonne Zhou, Mingyu Liang, Ivan Brugere et al.
Major-Minor Mean Field Multi-Agent Reinforcement Learning
Kai Cui, Christian Fabian, Anam Tahir et al.
Understanding Forgetting in Continual Learning with Linear Regression
Meng Ding, Kaiyi Ji, Di Wang et al.
Quality-Diversity with Limited Resources
Ren-Jian Wang, Ke Xue, Cong Guan et al.
Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback
Asaf Cassel, Haipeng Luo, Aviv Rosenberg et al.
On the Independence Assumption in Neurosymbolic Learning
Emile van Krieken, Pasquale Minervini, Edoardo Ponti et al.
diff History for Neural Language Agents
Ulyana Piterbarg, Lerrel Pinto, Rob Fergus
Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling
Raunaq Bhirangi, Chenyu Wang, Venkatesh Pattabiraman et al.
Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?
Khashayar Gatmiry, Nikunj Saunshi, Sashank J. Reddi et al.
CuTS: Customizable Tabular Synthetic Data Generation
Mark Vero, Mislav Balunovic, Martin Vechev
Instruction Tuning for Secure Code Generation
Jingxuan He, Mark Vero, Gabriela Krasnopolska et al.
Mimicking Better by Matching the Approximate Action Distribution
Joao A. Candido Ramos, Lionel Blondé, Naoya Takeishi et al.
Asymmetry in Low-Rank Adapters of Foundation Models
Jiacheng Zhu, Kristjan Greenewald, Kimia Nadjahi et al.
Slicing Mutual Information Generalization Bounds for Neural Networks
Kimia Nadjahi, Kristjan Greenewald, Rickard Gabrielsson et al.
Position: Fundamental Limitations of LLM Censorship Necessitate New Approaches
David Glukhov, Ilia Shumailov, Yarin Gal et al.
Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing
Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam et al.
Bifurcated Attention for Single-Context Large-Batch Sampling
Ben Athiwaratkun, Sujan Kumar Gonugondla, Sanjay Krishna Gouda et al.
Breadth-First Exploration on Adaptive Grid for Reinforcement Learning
Youngsik Yoon, Gangbok Lee, Sungsoo Ahn et al.
Smoothing Proximal Gradient Methods for Nonsmooth Sparsity Constrained Optimization: Optimality Conditions and Global Convergence
Ganzhao Yuan
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training
Ming-Kun Xie, Jia-Hao Xiao, Pei Peng et al.
Learning to Model the World With Language
Jessy Lin, Yuqing Du, Olivia Watkins et al.
The Merit of River Network Topology for Neural Flood Forecasting
Nikolas Kirschstein, Yixuan Sun
Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation
Fengdi Che, Chenjun Xiao, Jincheng Mei et al.
Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making
Vivek Myers, Chongyi Zheng, Anca Dragan et al.
Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making
Parand A. Alamdari, Toryn Q. Klassen, Elliot Creager et al.
Disguised Copyright Infringement of Latent Diffusion Models
Yiwei Lu, Matthew Yang, Zuoqiu Liu et al.
Predictive Linear Online Tracking for Unknown Targets
Anastasios Tsiamis, Aren Karapetyan, Yueshan Li et al.
Learning Latent Dynamic Robust Representations for World Models
Ruixiang Sun, Hongyu Zang, Xin Li et al.
Stealing part of a production language model
Nicholas Carlini, Daniel Paleka, Krishnamurthy Dvijotham et al.
Clifford-Steerable Convolutional Neural Networks
Maksim Zhdanov, David Ruhe, Maurice Weiler et al.
Sub-token ViT Embedding via Stochastic Resonance Transformers
Dong Lao, Yangchao Wu, Tian Yu Liu et al.
Dynamic Metric Embedding into lp Space
Kiarash Banihashem, MohammadTaghi Hajiaghayi, Dariusz Kowalski et al.
Graph2Tac: Online Representation Learning of Formal Math Concepts
Lasse Blaauwbroek, Mirek Olšák, Jason Rute et al.
Diffusion Language Models Are Versatile Protein Learners
Xinyou Wang, Zaixiang Zheng, Fei YE et al.
BWS: Best Window Selection Based on Sample Scores for Data Pruning across Broad Ranges
Hoyong Choi, Nohyun Ki, Hye Won Chung
Localizing Task Information for Improved Model Merging and Compression
Ke Wang, Nikolaos Dimitriadis, Guillermo Ortiz-Jimenez et al.
Detecting and Identifying Selection Structure in Sequential Data
Yujia Zheng, Zeyu Tang, Yiwen Qiu et al.
Data Engineering for Scaling Language Models to 128K Context
Yao Fu, Rameswar Panda, Xinyao Niu et al.
OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models
Fuzhao Xue, Zian Zheng, Yao Fu et al.
The Emergence of Reproducibility and Consistency in Diffusion Models
Huijie Zhang, Jinfan Zhou, Yifu Lu et al.
Accelerated Speculative Sampling Based on Tree Monte Carlo
Zhengmian Hu, Heng Huang
Random Latent Exploration for Deep Reinforcement Learning
Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari et al.