ICML 2025

3,330 papers

Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environments

Yun Qu, Cheems Wang, Yixiu Mao, Yiqin Lv, Xiangyang Ji

AEQA-NAT : Adaptive End-to-end Quantization Alignment Training Framework for Non-autoregressive Machine Translation

Xiangyu Qu, Guojing Liu, Liang Li

TabICL: A Tabular Foundation Model for In-Context Learning on Large Data

Jingang Qu, David Holzmüller, Gael Varoquaux, Marine Le Morvan

P(all-atom) Is Unlocking New Path For Protein Design

Wei Qu, Jiawei Guan, Rui Ma, Ke Zhai, Weikun Wu

CoDy: Counterfactual Explainers for Dynamic Graphs

Zhan Qu, Daniel Gomm, Michael Färber

RISE: Radius of Influence based Subgraph Extraction for 3D Molecular Graph Explanation

Jingxiang Qu, Wenhan Gao, Jiaxing Zhang, Xufeng Liu, Hua Wei

ROS: A GNN-based Relax-Optimize-and-Sample Framework for Max-k-Cut Problems

Yeqing Qiu, Ye XUE, Akang Wang, Yiheng Wang, Qingjiang Shi

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks

Shikai Qiu, Lechao Xiao, Andrew Gordon Wilson, Jeffrey Pennington, Atish Agarwala

Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing

Zijie Qiu, Jiaqi Wei, Xiang Zhang, Sheng Xu, Kai Zou

Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networks

Keyue Qiu, Yuxuan Song, Jie Yu, Hongbo Ma, Ziyao Cao

Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule

Keyue Qiu, Yuxuan Song, Zhehuan Fan, Peidong Liu, Zhe Zhang

Efficient Graph Continual Learning via Lightweight Graph Neural Tangent Kernels-based Dataset Distillation

Rihong Qiu, Xinke Jiang, Yuchen Fang, Hongbin Lai, Hao Miao

MindLLM: A Subject-Agnostic and Versatile Model for fMRI-to-text Decoding

Weikang Qiu, Zheng Huang, Haoyu Hu, Aosong Feng, Yujun Yan

The Lock-in Hypothesis: Stagnation by Algorithm

Tianyi Qiu, Zhonghao He, Tejasveer Chugh, Max Kleiman-Weiner

Feature out! Let Raw Image as Your Condition for Blind Face Restoration

Xinmin Qiu, Chen Gege, Bonan Li, Congying Han, Tiande Guo

Lightweight-Mark: Rethinking Deep Learning-Based Watermarking

Yupeng Qiu, Han Fang, Ee-Chien Chang

Exploiting Curvature in Online Convex Optimization with Delayed Feedback

Hao Qiu, Emmanuel Esposito, Mengxiao Zhang

Learning to Quantize for Training Vector-Quantized Networks

Peijia Qin, Jianguo Zhang

MixBridge: Heterogeneous Image-to-Image Backdoor Attack through Mixture of Schrödinger Bridges

Shixi Qin, Zhiyong Yang, Shilong Bao, Shi Wang, Qianqian Xu

Info-Coevolution: An Efficient Framework for Data Model Coevolution

Ziheng Qin, Hailun Xu, Wei Chee Yew, Qi Jia, Yang Luo

Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations

Jason Qin, Hans-Hermann Wessels, Carlos Fernandez-Granda, Yuhan Hao

WorldSimBench: Towards Video Generation Models as World Simulators

Yiran Qin, Zhelun Shi, Jiwen Yu, Xijun Wang, Enshen Zhou

Robust Consensus Anchor Learning for Efficient Multi-view Subspace Clustering

Yalan Qin, Nan Pu, Guorui Feng, Nicu Sebe

DeFoG: Discrete Flow Matching for Graph Generation

Yiming Qin, Manuel Madeira, Dorina Thanou, Pascal Frossard

GradPS: Resolving Futile Neurons in Parameter Sharing Network for Multi-Agent Reinforcement Learning

Haoyuan Qin, Zhengzhu Liu, Chenxing Lin, Chennan Ma, Songzhu Mei

Fast Incomplete Multi-view Clustering by Flexible Anchor Learning

Yalan Qin, Guorui Feng, Xinpeng Zhang

Enhancing Spectral GNNs: From Topology and Perturbation Perspectives

Taoyang Qin, Ke-Jia Chen, Zheng Liu

Large Continual Instruction Assistant

Jingyang Qiao, Zhizhong Zhang, Xin Tan, Yanyun Qu, Shouhong Ding

How Distributed Collaboration Influences the Diffusion Model Training? A Theoretical Perspective

Jing Qiao, Yu Liu, Yuan Yuan, Xiao Zhang, zhipeng cai

PDUDT: Provable Decentralized Unlearning under Dynamic Topologies

Jing Qiao, Yu Liu, Zengzhe Chen, Mingyi Li, Yuan Yuan

Structure-informed Risk Minimization for Robust Ensemble Learning

Fengchun Qiao, Yanlin Chen, Xi Peng

GCAL: Adapting Graph Models to Evolving Domain Shifts

Ziyue Qiao, Qianyi Cai, Hao Dong, Jiawei Gu, Pengyang Wang

On the Out-of-Distribution Generalization of Self-Supervised Learning

Wenwen Qiang, Jingyao Wang, Zeen Song, Jiangmeng Li, Changwen Zheng

TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree

Yu-Yang Qian, Yuan-Ze Xu, Zhen-Yu Zhang, Peng Zhao, Zhi-Hua Zhou

GHOST: Generalizable One-Shot Federated Graph Learning with Proxy-Based Topology Knowledge Retention

Jiaru Qian, Guancheng Wan, Wenke Huang, Guibin Zhang, Yuxin Wu

Universal Approximation Theorem of Deep Q-Networks

Qian Qi

Rethinking the Temperature for Federated Heterogeneous Distillation

Fan Qi, Daxu Shi, Chuokun Xu, Shuai Li, Changsheng Xu

Learning State-Based Node Representations from a Class Hierarchy for Fine-Grained Open-Set Detection

Spandan Pyakurel, Qi Yu

Sample Efficient Demonstration Selection for In-Context Learning

Kiran Purohit, Venktesh V, Sourangshu Bhattacharya, Avishek Anand

SHE: Streaming-media Hashing Retrieval

Ruitao Pu, Yang Qin, Xiaomin Song, Dezhong Peng, Zhenwen Ren

Janus: Dual-Server Multi-Round Secure Aggregation with Verifiability for Federated Learning

Lang Pu, Jingjing Gu, Chao Lin, Xinyi Huang

Clone-Robust AI Alignment

Ariel D. Procaccia, Benjamin Schiffer, Shirley Zhang

Learning dynamics in linear recurrent neural networks

Alexandra Maria Proca, Clémentine Carla Juliette Dominé, Murray Shanahan, Pedro A. M. Mediano

WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales

Drew Prinster, Xing Han, Anqi Liu, Suchi Saria

Graph Inverse Style Transfer for Counterfactual Explainability

Bardh Prenkaj, Efstratios Zaradoukas, Gjergji Kasneci

Be a Goldfish: Forgetting Bad Conditioning in Sparse Linear Regression via Variational Autoencoders

Kuheli Pratihar, Debdeep Mukhopadhyay

DCBM: Data-Efficient Visual Concept Bottleneck Models

Katharina Prasse, Patrick Knab, Sascha Marton, Christian Bartelt, Margret Keuper

Self-Consistency Preference Optimization

Archiki Prasad, Weizhe Yuan, Richard Yuanzhe Pang, Jing Xu, Maryam Fazel-Zarandi

MOGIC: Metadata-infused Oracle Guidance for Improved Extreme Classification

Suchith Chidananda Prabhu, Bhavyajeet Singh, Anshul Mittal, Siddarth Asokan, Shikhar Mohan

DEALing with Image Reconstruction: Deep Attentive Least Squares

Mehrsa Pourya, Erich Kobler, Michael Unser, Sebastian Neumayer

Self-Improving Language Models for Evolutionary Program Synthesis: A Case Study on ARC-AGI

Julien Pourcel, Cédric Colas, Pierre-yves Oudeyer

The Synergy of LLMs & RL Unlocks Offline Learning of Generalizable Language-Conditioned Policies with Low-fidelity Data

Thomas Pouplin, Kasia Kobalczyk, Hao Sun, Mihaela van der Schaar

Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings

Angéline Pouget, Mohammad Yaghini, Stephan Rabanser, Nicolas Papernot

Vintix: Action Model via In-Context Reinforcement Learning

Andrei Polubarov, Lyubaykin Nikita, Alexander Derevyagin, Ilya Zisman, Denis Tarasov

Avoiding Catastrophe in Online Learning by Asking for Help

Benjamin Plaut, Hanlin Zhu, Stuart Russell

To Each Metric Its Decoding: Post-Hoc Optimal Decision Rules of Probabilistic Hierarchical Classifiers

Roman Plaud, Alexandre Perez-Lebel, Matthieu Labeau, Antoine Saillenfest, Thomas Bonald

Rectifying Conformity Scores for Better Conditional Coverage

Vincent Plassier, Alexander Fishkov, Victor Dheur, Mohsen Guizani, Souhaib Ben Taieb

Do Bayesian Neural Networks Actually Behave Like Bayesian Models?

Gábor Pituk, Vik Shirvaikar, Tom Rainforth

Constrained Belief Updates Explain Geometric Structures in Transformer Representations

Mateusz Piotrowski, Paul M. Riechers, Daniel Filan, Adam Shai

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity

Alessandro Pierro, Steven Abreu, Jonathan Timcheck, Philipp Stratmann, Andreas Wild

Diffusion models for Gaussian distributions: Exact solutions and Wasserstein errors

Emile Pierret, Bruno Galerne

Privacy Amplification Through Synthetic Data: Insights from Linear Regression

Clément Pierquin, Aurélien Bellet, Marc Tommasi, Matthieu Boussard

Revisiting Unbiased Implicit Variational Inference

Tobias Pielok, Bernd Bischl, David Rügamer

Conditioning Diffusions Using Malliavin Calculus

Jakiw Pidstrigach, Elizabeth Louise Baker, Carles Domingo-Enrich, George Deligiannidis, Nikolas Nüsken

Boosting Masked ECG-Text Auto-Encoders as Discriminative Learners

Manh Pham Hung, Aaqib Saeed, Dong Ma

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models

Ngoc-Quan Pham, Tuan Truong, Quyen Tran, Tan Minh Nguyen, Dinh Phung

Discrete Markov Probabilistic Models: An Improved Discrete Score-Based Framework with sharp convergence bounds under minimal assumptions

Le-Tuyet-Nhi Pham, Dario Shariatian, Antonio Ocello, Giovanni Conforti, Alain Oliviero Durmus

Gandalf the Red: Adaptive Security for LLMs

Niklas Pfister, Václav Volhejn, Manuel Knott, Santiago Arias, Julia Bazinska

Wasserstein Policy Optimization

David Pfau, Ian Davies, Diana L Borsa, João Guilherme Madeira Araújo, Brendan Daniel Tracey

Hierarchical Planning for Complex Tasks with Knowledge Graph-RAG and Symbolic Verification

Flavio Petruzzellis, Cristina Cornelio, Pietro Lió

Training Deep Learning Models with Norm-Constrained LMOs

Thomas Pethick, Wanyun Xie, Kimon Antonakopoulos, Zhenyu Zhu, Antonio Silveti-Falls

Partition First, Embed Later: Laplacian-Based Feature Partitioning for Refined Embedding and Visualization of High-Dimensional Data

Erez Peterfreund, Ofir Lindenbaum, Yuval Kluger, Boris Landa

Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?

Guiomar Pescador-Barrios, Sarah Lucie Filippi, Mark van der Wilk

Mechanistic PDE Networks for Discovery of Governing Equations

Adeel Pervez, Efstratios Gavves, Francesco Locatello

Revisiting the Predictability of Performative, Social Events

Juan Carlos Perdomo

Noise Conditional Variational Score Distillation

Xinyu Peng, Ziyang Zheng, Yaoming Wang, Han Li, Nuowen Kan

Circumventing Backdoor Space via Weight Symmetry

Jie Peng, Hongwei Yang, Jing Zhao, Hengji Dong, Hui He

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models

Sen Peng, Mingyue Wang, Jianfei He, Jijia Yang, Xiaohua Jia

A Near Linear Query Lower Bound for Submodular Maximization

Binghui Peng, Aviad Rubinstein

KGMark: A Diffusion Watermark for Knowledge Graphs

Hongrui Peng, Haolang Lu, Yuanlong Yu, Weiye Fu, Kun Wang

Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach

Qian Peng, Yajie Bao, Haojie Ren, Zhaojun Wang, Changliang Zou

Modalities Contribute Unequally: Enhancing Medical Multi-modal Learning through Adaptive Modality Token Re-balancing

Jie Peng, Jenna L. Ballard, Mohan Zhang, Sukwon Yun, Jiayi Xin

Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization

Emiliano Penaloza, Tianyue H. Zhang, Laurent Charlin, Mateo Espinosa Zarlenga

Approximating Latent Manifolds in Neural Networks via Vanishing Ideals

Nico Pelleriti, Max Zimmer, Elias Samuel Wirth, Sebastian Pokutta

Hyper-Transforming Latent Diffusion Models

Ignacio Peis, Batuhan Koyuncu, Isabel Valera, Jes Frellsen

GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction

Muleilan Pei, Shaoshuai Shi, Lu Zhang, Peiliang Li, Shaojie Shen

Diffusion-based Adversarial Purification from the Perspective of the Frequency Domain

Gaozheng Pei, Ke Ma, Yingfei Sun, Qianqian Xu, Qingming Huang

Non-Stationary Predictions May Be More Informative: Exploring Pseudo-Labels with a Two-Phase Pattern of Training Dynamics

Hongbin Pei, Jingxin Hai, Yu Li, Huiqi Deng, Denghao Ma

Thermalizer: Stable autoregressive neural emulation of spatiotemporal chaos

Christian Pedersen, Laure Zanna, Joan Bruna

Permutation Equivariant Neural Networks for Symmetric Tensors

Edward Pearce-Crump

Compact Matrix Quantum Group Equivariant Neural Networks

Edward Pearce-Crump

Scaling Laws for Pre-training Agents and World Models

Tim Pearce, Tabish Rashid, David Bignell, Raluca Georgescu, Sam Devlin

Representative Language Generation

Charlotte Peale, Vinod Raman, Omer Reingold

Stable Offline Value Function Learning with Bisimulation-based Representations

Brahma S Pavse, Yudong Chen, Qiaomin Xie, Josiah P. Hanna

Automated Red Teaming with GOAT: the Generative Offensive Agent Tester

Maya Pavlova, Erik Brinkman, Krithika Iyer, Vı́tor Albiero, Joanna Bitton

AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs

Anselm Paulus, Arman Zharmagambetov, Chuan Guo, Brandon Amos, Yuandong Tian

Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation

Jan Pauls, Max Zimmer, Berkant Turan, Sassan Saatchi, Philippe Ciais

Automatically Interpreting Millions of Features in Large Language Models

Gonçalo Santos Paulo, Alex Troy Mallen, Caden Juang, Nora Belrose

The Berkeley Function Calling Leaderboard (BFCL): From Tool Use to Agentic Evaluation of Large Language Models

Shishir G Patil, Huanzhi Mao, Fanjia Yan, Charlie Cheng-Jie Ji, Vishnu Suresh

On the Local Complexity of Linear Regions in Deep ReLU Networks

Niket Nikul Patel, Guido Montúfar