ICML 2025

3,330 papers

Learning Minimum-Size BDDs: Towards Efficient Exact Algorithms

Christian Komusiewicz, Andre Schidler, Frank Sommer, Manuel Sorge, Luca Pascal Staus

NMA-tune: Generating Highly Designable and Dynamics Aware Protein Backbones

Urszula Julia Komorowska, Francisco Vargas, Alessandro Rondina, Pietro Lió, Mateja Jamnik

Certified Unlearning for Neural Networks

Anastasia Koloskova, Youssef Allouah, Animesh Jha, Rachid Guerraoui, Sanmi Koyejo

Feature-Mapping Topology Optimization with Neural Heaviside Signed Distance Functions

Aleksandr Kolomeitsev, Anh-Huy Phan

A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks

Varshita Kolipaka, Akshit Sinha, Debangan Mishra, Sumit Kumar, Arvindh Arun

Field Matching: an Electrostatic Paradigm to Generate and Transfer Data

Alexander Kolesov, S. I. Manukhov, Vladimir Vladimirovich Palyulin, Alexander Korotin

Rank-One Modified Value Iteration

Arman Sharifi Kolarijani, Tolga Ok, Peyman Mohajerin Esfahani, Mohamad Amin Sharifi Kolarijani

The Noisy Laplacian: a Threshold Phenomenon for Non-Linear Dimension Reduction

Alex Kokot, Octavian-Vlad Murad, Marina Meila

Exact Upper and Lower Bounds for the Output Distribution of Neural Networks with Random Inputs

Andrey Kofnov, Daniel Kapla, Ezio Bartocci, Efstathia Bura

Overestimation in LLM Evaluation: A Controlled Large-Scale Study on Data Contamination’s Impact on Machine Translation

Muhammed Yusuf Kocyigit, Eleftheria Briakou, Daniel Deutsch, Jiaming Luo, Colin Cherry

Gravity-Bench-v1: A Benchmark on Gravitational Physics Discovery for Agents

Nolan Koblischke, Hyunseok Jang, Kristen Menou, Mohamad Ali-Dib

Preference Learning for AI Alignment: a Causal Perspective

Kasia Kobalczyk, Mihaela van der Schaar

DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMs

Jongwoo Ko, Tianyi Chen, Sungnyun Kim, Tianyu Ding, Luming Liang

BAnG: Bidirectional Anchored Generation for Conditional RNA Design

Roman Klypa, Alberto Bietti, Sergei Grudinin

Fleet of Agents: Coordinated Problem Solving with Large Language Models

Lars Henning Klein, Nearchos Potamitis, Roland Aydin, Robert West, Caglar Gulcehre

Policy Design for Two-sided Platforms with Participation Dynamics

Haruka Kiyohara, Fan Yao, Sarah Dean

Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents

Shayan Kiyani, George J. Pappas, Aaron Roth, Hamed Hassani

Shielded Diffusion: Generating Novel and Diverse Images using Sparse Repellency

Michael Kirchhof, James Thornton, Louis Béthune, Pierre Ablin, Eugene Ndiaye

Improving Out-of-Distribution Detection with Markov Logic Networks

Konstantin Kirchheim, Frank Ortmeier

Optimal Transport Barycenter via Nonconvex-Concave Minimax Optimization

Kaheon Kim, Rentian Yao, Changbo Zhu, Xiaohui Chen

Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization

Kyurae Kim, Zuheng Xu, Jacob R. Gardner, Trevor Campbell

Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation

Juno Kim, Denny Wu, Jason D. Lee, Taiji Suzuki

Penalizing Infeasible Actions and Reward Scaling in Reinforcement Learning with Offline Data

Jeonghye Kim, Yongjae Shin, Whiyoung Jung, Sunghoon Hong, Deunsol Yoon

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Jaeyeon Kim, Kulin Shah, Vasilis Kontonis, Sham M. Kakade, Sitan Chen

Task-Aware Virtual Training: Enhancing Generalization in Meta-Reinforcement Learning for Out-of-Distribution Tasks

Jeongmo Kim, Yisak Park, Minung Kim, Seungyul Han

Linear Bandits with Partially Observable Features

Wonyoung Kim, Sungwoo Park, Garud Iyengar, Assaf Zeevi, Min-hwan Oh

Lexico: Extreme KV Cache Compression via Sparse Coding over Universal Dictionaries

Junhyuck Kim, Jongho Park, Jaewoong Cho, Dimitris Papailiopoulos

Test-Time Adaptation for Online Vision-Language Navigation with Feedback-based Reinforcement Learning

Sungjune Kim, Gyeongrok Oh, Heeju Ko, Daehyun Ji, Dongwook Lee

Navigating the Social Welfare Frontier: Portfolios for Multi-objective Reinforcement Learning

Cheol Woo Kim, Jai Moondra, Shresth Verma, Madeleine Pollack, Lingkai Kong

Efficient Generative Modeling with Residual Vector Quantization-Based Tokens

Jaehyeon Kim, Taehong Moon, Keon Lee, Jaewoong Cho

Causality-Aware Contrastive Learning for Robust Multivariate Time-Series Anomaly Detection

HyunGi Kim, Jisoo Mok, Dongjun Lee, Jaihyun Lew, Sungjae Kim

Flow-field inference from neural data using deep recurrent networks

Timothy Doyeon Kim, Thomas Zhihao Luo, Tankut Can, Kamesh Krishnamurthy, Jonathan W. Pillow

HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training

Geon-Woo Kim, Junbo Li, Shashidhar Gandham, Omar Baldonado, Adithya Gangidi

Provable Benefit of Random Permutations over Uniform Sampling in Stochastic Coordinate Descent

Donghwa Kim, Jaewook Lee, Chulhee Yun

TransPL: VQ-Code Transition Matrices for Pseudo-Labeling of Time Series Unsupervised Domain Adaptation

Jaeho Kim, Seulki Lee

NBDI: A Simple and Effective Termination Condition for Skill Extraction from Task-Agnostic Demonstrations

Myunsoo Kim, Hayeong Lee, Seong-Woong Shim, Junho Seo, Byung-Jun Lee

Peri-LN: Revisiting Normalization Layer in the Transformer Architecture

Jeonghoon Kim, Byeongchan Lee, Cheonbok Park, Yeontaek Oh, Beomjun Kim

Fair Clustering via Alignment

Kunwoong Kim, Jihu Lee, Sangchul Park, Yongdai Kim

Symmetry-Aware GFlowNets

Hohyun Kim, Seunggeun Lee, Min-hwan Oh

Thickness-aware E(3)-Equivariant 3D Mesh Neural Networks

Sungwon Kim, Namkyeong Lee, Yunyoung Doh, Seungmin Shin, Guimok Cho

Improved Off-policy Reinforcement Learning in Biological Sequence Design

Hyeonah Kim, Minsu Kim, Taeyoung Yun, Sanghyeok Choi, Emmanuel Bengio

Measuring Representational Shifts in Continual Learning: A Linear Transformation Perspective

Joonkyu Kim, Yejin Kim, Jy-yong Sohn

Federated Learning for Feature Generalization with Convex Constraints

Dongwon Kim, Donghee Kim, Sung Kuk Shyn, Kwangsu Kim

LoRA Training Provably Converges to a Low-Rank Global Minimum Or It Fails Loudly (But it Probably Won’t Fail)

Junsu Kim, Jaeyeon Kim, Ernest K. Ryu

Active Learning with Selective Time-Step Acquisition for PDEs

Yegon Kim, Hyunsu Kim, Gyeonghoon Ko, Juho Lee

Do Not Mimic My Voice : Speaker Identity Unlearning for Zero-Shot Text-to-Speech

Taesoo Kim, Jinju Kim, Dong Chan Kim, Jong Hwan Ko, Gyeong-Moon Park

When Model Knowledge meets Diffusion Model: Diffusion-assisted Data-free Image Synthesis with Alignment of Domain and Class

Yujin Kim, Hyunsoo Kim, Hyunwoo J. Kim, Suhyun Kim

BoA: Attention-aware Post-training Quantization without Backpropagation

Junhan Kim, Ho-young Kim, Eulrang Cho, Chungman Lee, Joonyoung Kim

MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech Recognition

Sungnyun Kim, Kangwook Jang, Sangmin Bae, Sungwoo Cho, Se-Young Yun

K$^2$IE: Kernel Method-based Kernel Intensity Estimators for Inhomogeneous Poisson Processes

Hideaki Kim, Tomoharu Iwata, Akinori Fujino

SPD: Sync-Point Drop for Efficient Tensor Parallelism of Large Language Models

Han-Byul Kim, Duc N.M Hoang, Arnav Kundu, Mohammad Samragh, Minsik Cho

NegMerge: Sign-Consensual Weight Merging for Machine Unlearning

Hyo Seo Kim, Dongyoon Han, Junsuk Choe

Correlated Errors in Large Language Models

Elliot Myunghoon Kim, Avi Garg, Kenny Peng, Nikhil Garg

GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance

Jinuk Kim, Marwa El Halabi, Wonpyo Park, Clemens Js Schaefer, Deokjae Lee

Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning

Jungtaek Kim

Neural Genetic Search in Discrete Spaces

Hyeonah Kim, Sanghyeok Choi, Jiwoo Son, Jinkyoo Park, Changhyun Kwon

Incremental Gradient Descent with Small Epoch Counts is Surprisingly Slow on Ill-Conditioned Problems

Yujun Kim, Jaeyoung Cha, Chulhee Yun

Hierarchical Reinforcement Learning with Targeted Causal Interventions

Mohammadsadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash, Matthias Grossglauser

Safely Learning Optimal Auctions: A Testable Learning Framework for Mechanism Design

Vikram Kher, Manolis Zampetakis

Scaling Sparse Feature Circuits For Studying In-Context Learning

Dmitrii Kharlapenko, Stepan Shabalin, Arthur Conmy, Neel Nanda

ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts

Samar Khanna, Medhanie Irgau, David B. Lobell, Stefano Ermon

FlexiClip: Locality-Preserving Free-Form Character Animation

Anant Khandelwal

SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity

Samir Khaki, Xiuyu Li, Junxian Guo, Ligeng Zhu, Konstantinos N. Plataniotis

Autoencoder-Based Hybrid Replay for Class-Incremental Learning

Milad Khademi Nori, IL MIN KIM, Guanghui Wang

ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy

Kian Kenyon-Dean, Zitong Jerry Wang, John Urbanik, Konstantin Donhauser, Jason Hartford

When and How Does CLIP Enable Domain and Compositional Generalization?

Elias Kempf, Simon Schrodi, Max Argus, Thomas Brox

Scalable Meta-Learning via Mixed-Mode Differentiation

Iurii Kemaev, Dan A. Calian, Luisa M Zintgraf, Gregory Farquhar, Hado van Hasselt

Bayesian Inference for Correlated Human Experts and Classifiers

Markelle Kelly, Alex James Boyd, Sam Showalter, Mark Steyvers, Padhraic Smyth

Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models

Armin Kekić, Sergio Hernan Garrido Mejia, Bernhard Schölkopf

Ultra Lowrate Image Compression with Semantic Residual Coding and Compression-aware Diffusion

Anle Ke, Xu Zhang, Tong Chen, Ming Lu, Chao Zhou

Inverse Reinforcement Learning with Switching Rewards and History Dependency for Characterizing Animal Behaviors

Jingyang Ke, Feiyang Wu, Jiyi Wang, Jeffrey Markowitz, Anqi Wu

Conservative Offline Goal-Conditioned Implicit V-Learning

Kaiqiang Ke, Qian Lin, Zongkai Liu, Shenghong He, Chao Yu

VinePPO: Refining Credit Assignment in RL Training of LLMs

Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, Alessandro Sordoni, Siva Reddy

Model-Based Exploration in Monitored Markov Decision Processes

Alireza Kazemipour, Matthew E. Taylor, Michael Bowling

Wyckoff Transformer: Generation of Symmetric Crystals

Nikita Kazeev, Wei Nong, Ignat Romanov, Ruiming Zhu, Andrey E Ustyuzhanin

Collapse or Thrive: Perils and Promises of Synthetic Data in a Self-Generating World

Joshua Kazdan, Rylan Schaeffer, Apratim Dey, Matthias Gerstgrasser, Rafael Rafailov

Bayesian Optimization from Human Feedback: Near-Optimal Regret Bounds

Aya Kayal, Sattar Vakili, Laura Toni, Da-shan Shiu, Alberto Bernacchia

Mixture of Experts Provably Detect and Learn the Latent Cluster Structure in Gradient-Based Learning

Ryotaro Kawata, Kohsei Matsutani, Yuri Kinoshita, Naoki Nishikawa, Taiji Suzuki

Leveraging Offline Data in Linear Latent Contextual Bandits

Chinmaya Kausik, Kevin Tan, Ambuj Tewari

Curvature Enhanced Data Augmentation for Regression

Ilya Kaufman, Omri Azencot

Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics

Tyler Kastner, Mark Rowland, Yunhao Tang, Murat A Erdogdu, Amir-massoud Farahmand

One Wave To Explain Them All: A Unifying Perspective On Feature Attribution

Gabriel Kasmi, Amandine Brunetto, Thomas FEL, Jayneel Parekh

SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability

Adam Karvonen, Can Rager, Johnny Lin, Curt Tigges, Joseph Isaac Bloom

PokéChamp: an Expert-level Minimax Language Agent

Seth Karten, Andy Luu Nguyen, Chi Jin

Implicit Regularization for Tubal Tensor Factorizations via Gradient Descent

Santhosh Karnik, Anna Veselovska, Mark Iwen, Felix Krahmer

Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms

Rickard Karlsson, Jh Krijthe

Devil is in the Details: Density Guidance for Detail-Aware Generation with Flow Models

Rafal Karczewski, Markus Heinonen, Vikas K Garg

The Power of Random Features and the Limits of Distribution-Free Gradient Descent

Ari Karchmer, Eran Malach

POROver: Improving Safety and Reducing Overrefusal in Large Language Models with Overgeneration and Preference Optimization

Batuhan K. Karaman, Ishmam Zabir, Alon Benhaim, Vishrav Chaudhary, Mert R. Sabuncu

Are Sparse Autoencoders Useful? A Case Study in Sparse Probing

Subhash Kantamneni, Joshua Engels, Senthooran Rajamanoharan, Max Tegmark, Neel Nanda

How Far Is Video Generation from World Model: A Physical Law Perspective

Bingyi Kang, Yang Yue, Rui Lu, Zhijie Lin, Yang Zhao

What Do Learning Dynamics Reveal About Generalization in LLM Mathematical Reasoning?

Katie Kang, Amrith Setlur, Dibya Ghosh, Jacob Steinhardt, Claire Tomlin

Revisiting Neural Networks for Few-Shot Learning: A Zero-Cost NAS Perspective

Haidong Kang

LRA-QViT: Integrating Low-Rank Approximation and Quantization for Robust and Efficient Vision Transformers

Beom Jin Kang, Namjoon Kim, Hyun Kim

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control

Zilin Kang, Chenyuan Hu, Yu Luo, Zhecheng Yuan, Ruijie Zheng

Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelity Image Manipulation

Mingyu Kang, Yong Suk Choi

SPEX: Scaling Feature Interaction Explanations for LLMs

Justin Singh Kang, Landon Butler, Abhineet Agarwal, Yigit Efe Erginbas, Ramtin Pedarsani

Algorithmic Recourse for Long-Term Improvement

Kentaro Kanamori, Ken Kobayashi, Satoshi Hara, Takuya Takagi

FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks

Zhaoxuan Kan, Husheng Han, Shangyi Shi, Tenghui Hua, Hang Lu

An analytic theory of creativity in convolutional diffusion models

Mason Kamb, Surya Ganguli