ICML Papers

Implicit Regularization for Tubal Tensor Factorizations via Gradient Descent

Santhosh Karnik, Anna Veselovska, Mark Iwen et al.

Implicit Riemannian Optimism with Applications to Min-Max Problems

Christophe Roux, David Martinez-Rubio, Sebastian Pokutta

Implicit Subgraph Neural Network

Yongjian Zhong, Liao Zhu, Hieu Vu et al.

Importance Corrected Neural JKO Sampling

Johannes Hertrich, Robert Gruhlke

Importance Sampling for Nonlinear Models

Prakash Palanivelu Rajmohan, Fred Roosta

Impossible Videos

Zechen Bai, Hai Ci, Mike Zheng Shou

Improved Algorithm for Deep Active Learning under Imbalance via Optimal Separation

Shyam Nuggehalli, Jifan Zhang, Lalit Jain et al.

Improved and Oracle-Efficient Online $\ell_1$-Multicalibration

Rohan Ghuge, Vidya Muthukumar, Sahil Singla

Improved Approximations for Hard Graph Problems using Predictions

Anders Aamand, Justin Chen, Siddharth Gollapudi et al.

Improved Coresets for Vertical Federated Learning: Regularized Linear and Logistic Regressions

Supratim Shit, Gurmehak chadha, Surendra kumar et al.

Improved Discretization Complexity Analysis of Consistency Models: Variance Exploding Forward Process and Decay Discretization Scheme

Ruofeng Yang, Bo Jiang, Cheng Chen et al.

Improved Expressivity of Hypergraph Neural Networks through High-Dimensional Generalized Weisfeiler-Leman Algorithms

Detian Zhang, Zhang Chengqiang, Yanghui Rao et al.

Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization

Zijian Liu, Zhengyuan Zhou

Improved Learning via k-DTW: A Novel Dissimilarity Measure for Curves

Amer Krivosija, Alexander Munteanu, André Nusser et al.

Improved Lower Bounds for First-order Stochastic Non-convex Optimization under Markov Sampling

Zhenyu Sun, Ermin Wei

Improved Off-policy Reinforcement Learning in Biological Sequence Design

Hyeonah Kim, Minsu Kim, Taeyoung Yun et al.

Improved Online Confidence Bounds for Multinomial Logistic Bandits

Joongkyu Lee, Min-hwan Oh

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance

Shogo Iwazaki, Shion Takeno

Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization

Guy Kornowski, Daogao Liu, Kunal Talwar

Improved Theoretically-Grounded Evolutionary Algorithms for Subset Selection with a Linear Cost Constraint

Dan-Xuan Liu, Chao Qian

Improving Compositional Generation with Diffusion Models Using Lift Scores

Chenning Yu, Sicun Gao

Improving Consistency Models with Generator-Augmented Flows

Thibaut Issenhuth, Sangchul Lee, Ludovic Dos Santos et al.

Improving Continual Learning Performance and Efficiency with Auxiliary Classifiers

Filip Szatkowski, Yaoyue Zheng, Fei Yang et al.

Improving Diversity in Language Models: When Temperature Fails, Change the Loss

Alexandre Verine, Florian Le Bronnec, Kunhao Zheng et al.

Improving Flow Matching by Aligning Flow Divergence

Yuhao Huang, Taos Transue, Shih-Hsin Wang et al.

Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging

Junkang Liu, Yuanyuan Liu, Fanhua Shang et al.

Improving Generalization with Flat Hilbert Bayesian Inference

Tuan Truong, Quyen Tran, Ngoc Quan Pham et al.

Improving LLM Safety Alignment with Dual-Objective Optimization

Xuandong Zhao, Will Cai, Tianneng Shi et al.

Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens

Ting-Ji Huang, Jia-Qi Yang, Chunxu Shen et al.

Improving LLM Video Understanding with 16 Frames Per Second

Yixuan Li, Changli Tang, Jimin Zhuang et al.

Improving Memory Efficiency for Training KANs via Meta Learning

Zhangchi Zhao, Jun Shu, Deyu Meng et al.

Improving Model Alignment Through Collective Intelligence of Open-Source Models

Junlin Wang, Roy Xie, Shang Zhu et al.

Improving Multi-Class Calibration through Normalization-Aware Isotonic Techniques

Alon Arad, Saharon Rosset

Improving Multimodal Learning Balance and Sufficiency through Data Remixing

Xiaoyu Ma, Hao Chen, Yongjian Deng

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration

Kaiyu Guo, Zijian Wang, Tan Pan et al.

Improving Out-of-Distribution Detection with Markov Logic Networks

Konstantin Kirchheim, Frank Ortmeier

Improving Parallel Program Performance with LLM Optimizers via Agent-System Interfaces

Anjiang Wei, Allen Nie, Thiago Teixeira et al.

Improving Rationality in the Reasoning Process of Language Models through Self-playing Game

Pinzheng Wang, Juntao Li, Zecheng Tang et al.

Improving Reward Model Generalization from Adversarial Process Enhanced Preferences

Zhilong Zhang, Tian Xu, Xinghao Du et al.

Improving Soft Unification with Knowledge Graph Embedding Methods

Xuanming Cui, Chionh Peng, Adriel Kuek et al.

Improving the Continuity of Goal-Achievement Ability via Policy Self-Regularization for Goal-Conditioned Reinforcement Learning

Xudong Gong, Sen Yang, Feng Dawei et al.

Improving the Diffusability of Autoencoders

Ivan Skorokhodov, Sharath Girish, Benran Hu et al.

Improving the Effective Receptive Field of Message-Passing Neural Networks

Shahaf E. Finder, Ron Shapira Weber, Moshe Eliasof et al.

Improving the Scaling Laws of Synthetic Data with Deliberate Practice

Reyhane Askari Hemmat, Mohammad Pezeshki, Elvis Dohmatob et al.

Improving the Statistical Efficiency of Cross-Conformal Prediction

Improving the Variance of Differentially Private Randomized Experiments through Clustering

Adel Javanmard, Vahab Mirrokni, Jean Pouget-Abadie

Improving Transformer World Models for Data-Efficient RL

Antoine Dedieu, Joseph Ortiz, Xinghua Lou et al.

Improving Value Estimation Critically Enhances Vanilla Policy Gradient

Tao Wang, Ruipeng Zhang, Sicun Gao

Improving Your Model Ranking on Chatbot Arena by Vote Rigging

Rui Min, Tianyu Pang, Chao Du et al.

Improving Zero-Shot Adversarial Robustness in Vision-Language Models by Closed-form Alignment of Adversarial Path Simplices

Junhao Dong, Piotr Koniusz, Yifei Zhang et al.