ICML 2025

3,330 papers

BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute

Dujian Ding, Ankur Mallick, Shaokun Zhang, Chi Wang, Daniel Madrigal

NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders

Jun-En Ding, Dongsheng Luo, Chenwei Wu, Feng Liu

Visual Abstraction: A Plug-and-Play Approach for Text-Visual Retrieval

Guofeng Ding, Yiding Lu, Peng Hu, Mouxing Yang, Yijie Lin

Scalable Non-Equivariant 3D Molecule Generation via Rotational Alignment

Yuhui Ding, Thomas Hofmann

MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models

Mahir Labib Dihan, Md Tanvir Hassan, Md Tanvir Parvez, Md Hasebul Hasan, Md Almash Alam

Score-based Pullback Riemannian Geometry: Extracting the Data Manifold Geometry using Anisotropic Flows

Willem Diepeveen, Georgios Batzolis, Zakhar Shumaylov, Carola-Bibiane Schönlieb

On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation

Nghiem Tuong Diep, Huy Nguyen, Chau Nguyen, Minh Le, Duy Minh Ho Nguyen

Statistical Query Hardness of Multiclass Linear Classification with Random Classification Noise

Ilias Diakonikolas, Mingchen Ma, Lisheng Ren, Christos Tzamos

Online Linear Classification with Massart Noise

Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis

On Fine-Grained Distinct Element Estimation

Ilias Diakonikolas, Daniel Kane, Jasper C.H. Lee, Thanasis Pittas, David Woodruff

Batch List-Decodable Linear Regression via Higher Moments

Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Sihan Liu, Thanasis Pittas

On Learning Parallel Pancakes with Mostly Uniform Weights

Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Jasper C.H. Lee, Thanasis Pittas

Efficient Multivariate Robust Mean Estimation Under Mean-Shift Contamination

Ilias Diakonikolas, Giannis Iakovidis, Daniel Kane, Thanasis Pittas

Optimizing Adaptive Attacks against Watermarks for Language Models

Abdulrahman Diaa, Toluwani Aremu, Nils Lukas

Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback

Qiwei Di, Jiafan He, Quanquan Gu

Continuous Bayesian Model Selection for Multivariate Causal Discovery

Anish Dhir, Ruby Sedgwick, Avinash Kori, Ben Glocker, Mark van der Wilk

A Unified Comparative Study with Generalized Conformity Scores for Multi-Output Conformal Regression

Victor Dheur, Matteo Fontana, Yorick Estievenart, Naomi Desobry, Souhaib Ben Taieb

Compressing tree ensembles through Level-wise Optimization and Pruning

Laurens Devos, Timo Martens, Deniz Can Oruc, Wannes Meert, Hendrik Blockeel

HashAttention: Semantic Sparsity for Faster Inference

Aditya Desai, Shuo Yang, Alejandro Cuadron, Matei Zaharia, Joseph E. Gonzalez

Theoretical Limitations of Ensembles in the Age of Overparameterization

Niclas Dern, John Patrick Cunningham, Geoff Pleiss

QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions

Zhun Deng, Thomas P Zollo, Benjamin Eyre, Amogh Inamdar, David Madras

EvoMesh: Adaptive Physical Simulation with Hierarchical Graph Evolutions

Huayu Deng, Xiangming Zhu, Yunbo Wang, Xiaokang Yang

Unlocking the Power of Rehearsal in Continual Learning: A Theoretical Perspective

Junze Deng, Qinhang Wu, Peizhong Ju, Sen Lin, Yingbin Liang

A Versatile Influence Function for Data Attribution with Non-Decomposable Loss

Junwei Deng, Weijing Tang, Jiaqi W. Ma

EGPlace: An Efficient Macro Placement Method via Evolutionary Search with Greedy Repositioning Guided Mutation

Ji Deng, Zhao Li, Ji Zhang, Jun Gao

Discrepancy Minimization in Input-Sparsity Time

Yichuan Deng, Xiaoyu Li, Zhao Song, OMRI WEINSTEIN

Procurement Auctions via Approximately Optimal Submodular Optimization

Yuan Deng, Amin Karbasi, Vahab Mirrokni, Renato Paes Leme, Grigoris Velegkas

Predicting mutational effects on protein binding from folding energy

Arthur Deng, Karsten D. Householder, Fang Wu, K. Christopher Garcia, Brian L. Trippe

FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing

Yingying Deng, Xiangyu He, Changwang Mei, Peisong Wang, Fan Tang

Towards Understanding Parametric Generalized Category Discovery on Graphs

Bowen Deng, Lele Fu, Jialong Chen, Sheng Huang, Tianchi Liao

Learning Distribution-wise Control in Representation Space for Language Models

Chunyuan Deng, Ruidi Chang, Hanjie Chen

Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets

Charita Dellaporta, Patrick O’Hara, Theodoros Damoulas

A Unified Approach to Routing and Cascading for LLMs

Jasper Dekoninck, Maximilian Baader, Martin Vechev

An Efficient Matrix Multiplication Algorithm for Accelerating Inference in Binary and Ternary Neural Networks

Mohsen Dehghankar, Mahdi Erfanian, Abolfazl Asudeh

Improving Transformer World Models for Data-Efficient RL

Antoine Dedieu, Joseph Ortiz, Xinghua Lou, Carter Wendelken, J Swaroop Guntupalli

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain

Rohan Deb, Kiran Koshy Thekumparampil, Kousha Kalantari, Gaurush Hiranandani, Shoham Sabach

Trust-Region Twisted Policy Improvement

Joery A. De Vries, Jinke He, Yaniv Oren, Matthijs T. J. Spaan

Optimization over Sparse Support-Preserving Sets: Two-Step Projection with Global Optimality Guarantees

William de Vazelhes, Xiaotong Yuan, Bin Gu

Wrapped Gaussian on the manifold of Symmetric Positive Definite Matrices

Thibault De Surrel, Fabien Lotte, Sylvain Chevallier, Florian Yger

Breaking the Barrier of Hard Samples: A Data-Centric Approach to Synthetic Data for Medical Tasks

Maynara Donato De Souza, Cleber Zanchettin

Provable Maximum Entropy Manifold Exploration via Diffusion Models

Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He

Enhancing Diversity In Parallel Agents: A Maximum State Entropy Exploration Story

Vincenzo De Paola, Riccardo Zamboni, Mirco Mutti, Marcello Restelli

Sliding Puzzles Gym: A Scalable Benchmark for State Representation in Visual Reinforcement Learning

Bryan Lincoln Marques De Oliveira, Luana Guedes Barros Martins, Bruno Brandão, Murilo Lopes Da Luz, Telma Woerle De Lima Soares

EncryptedLLM: Privacy-Preserving Large Language Model Inference via GPU-Accelerated Fully Homomorphic Encryption

Leo De Castro, Daniel Escudero, Adya Agrawal, Antigoni Polychroniadou, Manuela Veloso

Distributional Diffusion Models with Scoring Rules

Valentin De Bortoli, Alexandre Galashov, J Swaroop Guntupalli, Guangyao Zhou, Kevin Patrick Murphy

Accelerated Diffusion Models via Speculative Sampling

Valentin De Bortoli, Alexandre Galashov, Arthur Gretton, Arnaud Doucet

Learning Gaussian DAG Models without Condition Number Bounds

Constantinos Costis Daskalakis, Anthimos Vardis Kandiros, Rui Yao

A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings

Shib Sankar Dasgupta, Michael Boratko, Andrew McCallum

Retraining with Predicted Hard Labels Provably Increases Model Accuracy

Rudrajit Das, Inderjit S Dhillon, Alessandro Epasto, Adel Javanmard, Jieming Mao

Online Sparsification of Bipartite-Like Clusters in Graphs

Joyentanuj Das, Suranjan De, He Sun

Near-Optimal Consistency-Robustness Trade-Offs for Learning-Augmented Online Knapsack Problems

Mohammadreza Daneshvaramoli, Helia Karisani, Adam Lechowicz, Bo Sun, Cameron N Musco

Efficiently Vectorized MCMC on Modern Accelerators

Hugh Dance, Pierre Glaser, Peter Orbanz, Ryan P Adams

Shifting Time: Time-series Forecasting with Khatri-Rao Neural Operators

Srinath Dama, Kevin Course, Prasanth B. Nair

Monte-Carlo Tree Search with Uncertainty Propagation via Optimal Transport

Tuan Quang Dam, Pascal Stenger, Lukas Schneider, Joni Pajarinen, Carlo D’Eramo

Power Mean Estimation in Stochastic Continuous Monte-Carlo Tree Search

Tuan Quang Dam

Online Robust Reinforcement Learning Through Monte-Carlo Planning

Tuan Quang Dam, Kishan Panaganti, Brahim Driss, Adam Wierman

Efficient Noise Calculation in Deep Learning-based MRI Reconstructions

Onat Dalmaz, Arjun D Desai, Reinhard Heckel, Tolga Cukur, Akshay S Chaudhari

Sparse-pivot: Dynamic correlation clustering for node insertions

Mina Dalirrooyfard, Konstantin Makarychev, Slobodan Mitrovic

Policy Gradient with Tree Expansion

Gal Dalal, Assaf Hallak, Gugan Thoppe, Shie Mannor, Gal Chechik

MindAligner: Explicit Brain Functional Alignment for Cross-Subject Visual Decoding from Limited fMRI Data

Yuqin Dai, Zhouheng Yao, Chunfeng Song, Qihao Zheng, Weijian Mai

Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle

Hui Dai, Ryan Teehan, Mengye Ren

Latent Variable Causal Discovery under Selection Bias

Haoyue Dai, Yiwen Qiu, Ignavier Ng, Xinshuai Dong, Peter Spirtes

MemFreezing: A Novel Adversarial Attack on Temporal Graph Neural Networks under Limited Future Knowledge

Yue Dai, Liang Liu, Xulong Tang, Youtao Zhang, Jun Yang

Confidence Difference Reflects Various Supervised Signals in Confidence-Difference Classification

Yuanchao Dai, Ximing Li, Changchun Li

Learning With Multi-Group Guarantees For Clusterable Subpopulations

Jessica Dai, Nika Haghtalab, Eric Zhao

From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms

Jessica Dai, Paula Gradu, Inioluwa Deborah Raji, Benjamin Recht

Robust Secure Swap: Responsible Face Swap With Persons of Interest Redaction and Provenance Traceability

Yunshu Dai, Jianwei Fei, Fangjun Huang, Chip Hong Chang

SAN: Hypothesizing Long-Term Synaptic Development and Neural Engram Mechanism in Scalable Model’s Parameter-Efficient Fine-Tuning

Gaole Dai, Chun-Kai Fan, Yiming Tang, Zhi Zhang, yuan zhang

EnsLoss: Stochastic Calibrated Loss Ensembles for Preventing Overfitting in Classification

Ben Dai

Adapting to Evolving Adversaries with Regularized Continual Robust Training

Sihui Dai, Christian Cianfarani, Vikash Sehwag, Prateek Mittal, Arjun Bhagoji

CLIMB: Data Foundations for Large Scale Multimodal Clinical Foundation Models

Wei Dai, Peilin Chen, Malinda Lu, Daniel A Li, Haowen Wei

Continual Generalized Category Discovery: Learning and Forgetting from a Bayesian Perspective

Hao Dai, Jagmohan Chauhan

Generalization of noisy SGD in unbounded non-convex settings

Leello Tadesse Dadi, Volkan Cevher

Just Enough Shifts: Mitigating Over-Refusal in Aligned Language Models with Targeted Representation Fine-Tuning

Mahavir Dabas, Si Chen, Charles Fleming, Ming Jin, Ruoxi Jia

On Temperature Scaling and Conformal Prediction of Deep Classifiers

Lahav Dabah, Tom Tirer

Causal Abstraction Learning based on the Semantic Embedding Principle

Gabriele D’Acunto, Fabio Massimo Zennaro, Yorgos Felekis, Paolo Di Lorenzo

SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders

Bartosz Cywiński, Kamil Deja

Robust Autonomy Emerges from Self-Play

Marco Cusumano-Towner, David Hafner, Alexander Hertzberg, Brody Huval, Aleksei Petrenko

Flow-based Domain Randomization for Learning and Sequencing Robotic Skills

Aidan Curtis, Eric Li, Michael Noseworthy, Nishad Gothoskar, Sachin Chitta

Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model

Rachel Cummings, Alessandro Epasto, Jieming Mao, Tamalika Mukherjee, Tingting Ou

Enhancing Target-unspecific Tasks through a Features Matrix

Fangming Cui, Yonggang Zhang, Xuan Wang, Xinmei Tian, Jun Yu

Human Cognition-Inspired Hierarchical Fuzzy Learning Machine

Junbiao Cui, Qin Yue, Jianqing Liang, Jiye Liang

InfoSEM: A Deep Generative Model with Informative Priors for Gene Regulatory Network Inference

Tianyu Cui, Song-Jun Xu, Artem Moskalev, Shuwei Li, Tommaso Mansi

An Augmentation-Aware Theory for Self-Supervised Contrastive Learning

Jingyi Cui, Hongwei Wen, Yisen Wang

Calibrating Video Watch-time Predictions with Credible Prototype Alignment

Chao Cui, Shisong Tang, Fan Li, Jiechao Gao, Hechang Chen

Improving Soft Unification with Knowledge Graph Embedding Methods

Xuanming Cui, Chionh Wei Peng, Adriel Kuek, Ser-Nam Lim

OR-Bench: An Over-Refusal Benchmark for Large Language Models

Justin CUI, Wei-Lin Chiang, Ion Stoica, Cho-jui Hsieh

Prediction-Powered E-Values

Daniel Csillag, Claudio Jose Struchiner, Guilherme Tegoni Goedert

Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability

Michael Crawshaw, Blake Woodworth, Mingrui Liu

Edge-Colored Clustering in Hypergraphs: Beyond Minimizing Unsatisfied Edges

Alex Crane, Thomas Stanley, Blair D. Sullivan, Nate Veldt

No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets

Corinna Coupette, Jeremy Wayland, Emily Simons, Bastian Rieck

BARNN: A Bayesian Autoregressive and Recurrent Neural Network

Dario Coscia, Max Welling, Nicola Demo, Gianluigi Rozza

Learning-Augmented Algorithms for MTS with Bandit Access to Multiple Predictors

Matei Gabriel Cosa, Marek Elias

FAB-PPI: Frequentist, Assisted by Bayes, Prediction-Powered Inference

Stefano Cortinovis, Francois Caron

Data-driven Design of Randomized Control Trials with Guaranteed Treatment Effects

Santiago Cortes-Gomez, Naveen Janaki Raman, Aarti Singh, Bryan Wilder

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting

Adrien Cortés, Remi Rehm, Victor Letzelter

Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Data

Corinna Cortes, Anqi Mao, Mehryar Mohri, Yutao Zhong

Counterfactual Graphical Models: Constraints and Inference

Juan D. Correa, Elias Bareinboim

Kinetic Langevin Diffusion for Crystalline Materials Generation

François R J Cornet, Federico Bergamin, Arghya Bhowmik, Juan Maria Garcia-Lastra, Jes Frellsen

Algorithms and Hardness for Active Learning on Graphs

Vincent Cohen-Addad, Silvio Lattanzi, Simon Meierhans