ICML 2025

3,330 papers

A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs

Kihyuk Hong, Ambuj Tewari

Trajectory Inference with Smooth Schrödinger Bridges

Wanli Hong, Yuliang Shi, Jonathan Niles-Weed

CoCoA-Mix: Confusion-and-Confidence-Aware Mixture Model for Context Optimization

Dasol Hong, Wooju Lee, Hyun Myung

On the Robustness of Reward Models for Language Model Alignment

Jiwoo Hong, Noah Lee, Eunki Kim, Guijin Son, Woojin Chung

PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting for Novel View Synthesis

Sunghwan Hong, Jaewoo Jung, Heeseong Shin, Jisang Han, Jiaolong Yang

MTSTRec: Multimodal Time-Aligned Shared Token Recommender

Ming-Yi Hong, Yen-Jung Hsu, Miao-Chen Chiang, Che Lin

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need

Ziming Hong, Runnan Chen, Zengmao Wang, Bo Han, Bo Du

PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations

Benjamin Holzschuh, Qiang Liu, Georg Kohl, Nils Thuerey

G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration

Samuel Holt, Max Ruiz Luyten, Antonin Berthon, Mihaela van der Schaar

EvoControl: Multi-Frequency Bi-Level Control for High-Frequency Continuous Control

Samuel Holt, Todor Davchev, Dhruva Tirumala, Ben Moran, Atil Iscen

Open Materials Generation with Stochastic Interpolants

Philipp Höllmer, Thomas Egg, Maya Martirossyan, Eric Fuemmeler, Zeren Shui

LEAPS: A discrete neural sampler via locally equivariant networks

Peter Holderrieth, Michael Samuel Albergo, Tommi Jaakkola

Stochastic Poisson Surface Reconstruction with One Solve using Geometric Gaussian Processes

Sidhanth Holalkere, David Bindel, Silvia Sellán, Alexander Terenin

Uniform Mean Estimation for Heavy-Tailed Distributions via Median-of-Means

Mikael Møller Høgsgaard, Andrea Paudice

The Elicitation Game: Evaluating Capability Elicitation Techniques

Felix Hofstätter, Teun van der Weij, Jayden Teoh, Rada Djoneva, Henning Bartsch

Models of Heavy-Tailed Mechanistic Universality

Liam Hodgkinson, Zhichao Wang, Michael W. Mahoney

Logits are All We Need to Adapt Closed Models

Gaurush Hiranandani, Haolun Wu, Subhojyoti Mukherjee, Sanmi Koyejo

Guided Zeroth-Order Methods for Stochastic Non-convex Problems with Decision-Dependent Distributions

Yuya Hikima, Hiroshi Sawada, Akinori Fujino

Direct Density Ratio Optimization: A Statistically Consistent Approach to Aligning Large Language Models

Rei Higuchi, Taiji Suzuki

Generalization Analysis for Supervised Contrastive Representation Learning under Non-IID Settings

Nong Minh Hieu, Antoine Ledent

Flexible Tails for Normalizing Flows

Tennessee Hickling, Dennis Prangle

Decision-aware Training of Spatiotemporal Forecasting Models to Select a Top-K Subset of Sites for Intervention

Kyle Heuton, Frederick Muench, Shikhar Shrestha, Thomas J. Stopka, Michael C Hughes

Compression via Pre-trained Transformers: A Study on Byte-Level Multimodal Data

David Heurtel-Depeiges, Anian Ruoss, Joel Veness, Tim Genewein

Importance Corrected Neural JKO Sampling

Johannes Hertrich, Robert Gruhlke

Measuring In-Context Computation Complexity via Hidden State Prediction

Vincent Herrmann, Róbert Csordás, Jürgen Schmidhuber

Neurosymbolic World Models for Sequential Decision Making

Leonardo Hernandez Cano, Maxine Perroni-Scharf, Neil Dhir, Arun Ramamurthy, Armando Solar-Lezama

On the Duality between Gradient Transformations and Adapters

Lucas Torroba Hennigen, Hunter Lang, Han Guo, Yoon Kim

Secant Line Search for Frank-Wolfe Algorithms

Deborah Hendrych, Sebastian Pokutta, Mathieu Besançon, David Martı́nez-Rubio

Gradient Inversion of Multimodal Models

Omri Ben Hemo, Alon Zolfi, Oryan Yehezkel, Omer Hofman, Roman Vainshtein

LlavaGuard: An Open VLM-based Framework for Safeguarding Vision Datasets and Models

Lukas Helff, Felix Friedrich, Manuel Brack, Kristian Kersting, Patrick Schramowski

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features

Alec Helbling, Tuna Han Salih Meral, Benjamin Hoover, Pinar Yanardag, Duen Horng Chau

To Steer or Not to Steer? Mechanistic Error Reduction with Abstention for Language Models

Anna Hedström, Salim I. Amoukou, Tom Bewley, Saumitra Mishra, Manuela Veloso

Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design

Marcel Hedman, Desi R. Ivanova, Cong Guan, Tom Rainforth

DeepCrossAttention: Supercharging Transformer Residual Connections

Mike Heddes, Adel Javanmard, Kyriakos Axiotis, Gang Fu, Mohammadhossein Bateni

Towards Global-level Mechanistic Interpretability: A Perspective of Modular Circuits of Large Language Models

Yinhan He, Wendy Zheng, Yushun Dong, Yaochen Zhu, Chen Chen

RUN: Reversible Unfolding Network for Concealed Object Segmentation

Chunming He, Rihan Zhang, Fengyang Xiao, Chengyu Fang, Longxiang Tang

A Closer Look at Backdoor Attacks on CLIP

Shuo He, Zhifang Zhang, Feng Liu, Roy Ka-Wei Lee, Bo An

Revisiting Convergence: Shuffling Complexity Beyond Lipschitz Smoothness

Qi He, Peiran Yu, Ziyi Chen, Heng Huang

Primphormer: Efficient Graph Transformers with Primal Representations

Mingzhen He, Ruikai Yang, Hanling Tian, Youmei Qiu, Xiaolin Huang

Chaos Meets Attention: Transformers for Large-Scale Dynamical Prediction

Yi He, Yiming Yang, Xiaoyuan Cheng, Hai Wang, Xiao Xue

Primal-Dual Neural Algorithmic Reasoning

Yu He, Ellen Vitercik

In-Context Linear Regression Demystified: Training Dynamics and Mechanistic Interpretability of Multi-Head Softmax Attention

Jianliang He, Xintian Pan, Siyu Chen, Zhuoran Yang

FuseUNet: A Multi-Scale Feature Fusion Method for U-like Networks

Quansong He, Xiangde Min, Kaishen Wang, Tao He

GIVE: Structured Reasoning of Large Language Models with Knowledge Graph Inspired Veracity Extrapolation

Jiashu He, Mingyu Derek Ma, Jinxuan Fan, Dan Roth, Wei Wang

Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction

Yiting He, Zhishuai Liu, Weixin Wang, Pan Xu

Olica: Efficient Structured Pruning of Large Language Models without Retraining

Jiujun He, Huazhen Lin

Goal-Oriented Skill Abstraction for Offline Multi-Task Reinforcement Learning

Jinmin He, Kai Li, Yifan Zang, Haobo Fu, Qiang Fu

Going Deeper into Locally Differentially Private Graph Neural Networks

Longzhu He, Chaozhuo Li, Peng Tang, Sen Su

Learning Time-Aware Causal Representation for Model Generalization in Evolving Domains

Zhuo He, Shuang Li, Wenze Song, Longhui Yuan, Jian Liang

Subspace Optimization for Large Language Models with Convergence Guarantees

Yutong He, Pengrui Li, Yipeng Hu, Chuyan Chen, Kun Yuan

ReferSplat: Referring Segmentation in 3D Gaussian Splatting

Shuting He, Guangquan Jie, Changshuo Wang, Yun Zhou, Shuming Hu

Machines and Mathematical Mutations: Using GNNs to Characterize Quiver Mutation Classes

Jesse He, Helen Jenne, Herman Chau, Davis Brown, Mark Raugas

SPMC: Self-Purifying Federated Backdoor Defense via Margin Contribution

Wenwen He, Wenke Huang, Bin Yang, Shukan Liu, Mang Ye

Double-Filter: Efficient Fine-tuning of Pre-trained Vision-Language Models via Patch&Layer Filtering

Yaoqin He, Junchen Fu, Kaiwen Zheng, Songpei Xu, Fuhai Chen

Semantic Shift Estimation via Dual-Projection and Classifier Reconstruction for Exemplar-Free Class-Incremental Learning

Run He, Di Fang, Yicheng Xu, Yawen Cui, Ming Li

DA-KD: Difficulty-Aware Knowledge Distillation for Efficient Large Language Models

Changyi He, Yifu Ding, Jinyang Guo, Ruihao Gong, Haotong Qin

ZipAR: Parallel Autoregressive Image Generation through Spatial Locality

Yefei He, Feng Chen, Yuanyu He, Shaoxuan He, Hong Zhou

Random Policy Evaluation Uncovers Policies of Generative Flow Networks

Haoran He, Emmanuel Bengio, Qingpeng Cai, Ling Pan

Double Machine Learning for Causal Inference under Shared-State Interference

Chris Hays, Manish Raghavan

Generative Data Mining with Longtail-Guided Diffusion

David S Hayden, Mao Ye, Timur Garipov, Gregory P. Meyer, Carl Vondrick

Distillation of Discrete Diffusion through Dimensional Correlations

Satoshi Hayakawa, Yuhta Takida, Masaaki Imaizumi, Hiromi Wakaki, Yuki Mitsufuji

Wasserstein Flow Matching: Generative Modeling Over Families of Distributions

Doron Haviv, Aram-Alexandre Pooladian, Dana Pe’er, Brandon Amos

Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching

Aaron J Havens, Benjamin Kurt Miller, Bing Yan, Carles Domingo-Enrich, Anuroop Sriram

Optimal Decision Tree Pruning Revisited: Algorithms and Complexity

Juha Harviainen, Frank Sommer, Manuel Sorge, Stefan Szeider

Controlling Neural Collapse Enhances Out-of-Distribution Detection and Transfer Learning

Md Yousuf Harun, Jhair Gallardo, Christopher Kanan

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods

Yifan Hao, Xingyuan Pan, Hanning Zhang, Chenlu Ye, Rui Pan

Faster and Stronger: When ANN-SNN Conversion Meets Parallel Spiking Calculation

Zecheng Hao, Qichao Ma, Kang Chen, Yi Zhang, Zhaofei Yu

SafeMap: Robust HD Map Construction from Incomplete Observations

Xiaoshuai Hao, Lingdong Kong, Rong Yin, Pengwei Wang, Jing Zhang

Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark

Yunzhuo Hao, Jiawei Gu, Huichen Will Wang, Linjie Li, Zhengyuan Yang

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation

Philippe Hansen-Estruch, David Yan, Ching-Yao Chuang, Orr Zohar, Jialiang Wang

A Trichotomy for List Transductive Online Learning

Steve Hanneke, Amirreza Shaeiri

Representation Preserving Multiclass Agnostic to Realizable Reduction

Steve Hanneke, Qinglin Meng, Amirreza Shaeiri

Unveiling AI’s Blind Spots: An Oracle for In-Domain, Out-of-Domain, and Adversarial Errors

Shuangpeng Han, Mengmi Zhang

Heads up! Large Language Models Can Perform Tasks Without Your Instruction via Selective Attention Head Masking

Senyu Han, Hongchuan Zeng, Kai Yu, Lu Chen

Adaptive Median Smoothing: Adversarial Defense for Unlearned Text-to-Image Diffusion Models at Inference Time

Xiaoxuan Han, Songlin Yang, Wei Wang, Yang Li, Jing Dong

MoRAgent: Parameter Efficient Agent Tuning with Mixture-of-Roles

Jing Han, Binwei Yan, Tianyu Guo, Zheyuan Bai, Mengyu Zheng

Flex3D: Feed-Forward 3D Generation with Flexible Reconstruction Model and Input View Curation

Junlin Han, Jianyuan Wang, Andrea Vedaldi, Philip Torr, Filippos Kokkinos

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective

Seungwook Han, Jinyeop Song, Jeff Gore, Pulkit Agrawal

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence

Yinbin Han, Meisam Razaviyayn, Renyuan Xu

Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method

Andi Han, Pierre-Louis Poirion, Akiko Takeda

Ranked Entropy Minimization for Continual Test-Time Adaptation

Jisu Han, Jaemin Na, Wonjun Hwang

Retrieval Augmented Time Series Forecasting

Sungwon Han, Seungeon Lee, Meeyoung Cha, Sercan O Arik, Jinsung Yoon

On the Role of Label Noise in the Feature Learning Process

Andi Han, Wei Huang, Zhanpeng Zhou, Gang Niu, Wuyang Chen

Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images?

Yujin Han, Andi Han, Wei Huang, Chaochao Lu, Difan Zou

Of Mice and Machines: A Comparison of Learning Between Real World Mice and RL Agents

Shuo Han, German Espinosa, Junda Huang, Daniel Dombeck, Malcolm A. Maciver

Quantifying Prediction Consistency Under Fine-tuning Multiplicity in Tabular LLMs

Faisal Hamman, Pasan Dissanayake, Saumitra Mishra, Freddy Lecue, Sanghamitra Dutta

Hierarchical Refinement: Optimal Transport to Infinity and Beyond

Peter Halmos, Julian Gold, Xinhao Liu, Benjamin Raphael

Proactive Agents for Multi-Turn Text-to-Image Generation Under Uncertainty

Meera Hahn, Wenjun Zeng, Nithish Kannen, Rich Galt, Kartikeya Badola

Learning Extrapolative Sequence Transformations from Markov Chains

Sophia Hager, Aleem Khan, Andrew Wang, Nicholas Andrews

Predicting the Susceptibility of Examples to Catastrophic Forgetting

Guy Hacohen, Tinne Tuytelaars

Limitations of measure-first protocols in quantum machine learning

Casper Gyurik, Riccardo Molteni, Vedran Dunjko

Exponential Family Variational Flow Matching for Tabular Data Generation

Andrés Guzmán-Cordero, Floor Eijkelboom, Jan-Willem van de Meent

From RAG to Memory: Non-Parametric Continual Learning for Large Language Models

Bernal Jiménez Gutiérrez, Yiheng Shu, Weijian Qi, Sizhe Zhou, Yu Su

Inverse Bridge Matching Distillation

Nikita Gushchin, David Li, Daniil Selikhanovych, Evgeny Burnaev, Dmitry Baranchuk

Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metrics

Aleksandr Gushchin, Khaled Abud, Georgii Bychkov, Ekaterina Shumitskaya, Anna Chistyakova

HiRemate: Hierarchical Approach for Efficient Re-materialization of Neural Networks

Julia Gusak, Xunyi Zhao, Théotime Le Hellard, Zhe Li, Lionel Eyraud-Dubois

PEAKS: Selecting Key Training Examples Incrementally via Prediction Error Anchored by Kernel Similarity

Mustafa Burak Gurbuz, Xingyu Zheng, Constantine Dovrolis

AlphaPO: Reward Shape Matters for LLM Alignment

Aman Gupta, Shao Tang, Qingquan Song, Sirou Zhu, Jiwoo Hong

AMPO: Active Multi Preference Optimization for Self-play Preference Selection

Taneesh Gupta, Rahul Madhavan, Xuchao Zhang, Chetan Bansal, Saravan Rajmohan

A Machine Learning Approach to Duality in Statistical Physics

Prateek Gupta, Andrea E. V. Ferrari, Nabil Iqbal