International Conference on Machine Learning
ICML
A*
CORE rank
CORE rank (raw)
A*
Acceptance rate
27.5% (2024)
Fields of research
Machine Learning
Papers indexed
17,065
1988–2025
Papers per year
19883,341 peak2025
Most published authors
ICML papers
17,065 records sourced from DBLP. Search titles, filter by year, sort by recency.
| Year | Title | Authors |
|---|---|---|
| 2025 | Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts. | Lan Li, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan |
| 2025 | Tokenized Bandit for LLM Decoding and Alignment. | Suho Shin, Chenghao Yang, Haifeng Xu, MohammadTaghi Hajiaghayi |
| 2025 | ABKD: Pursuing a Proper Allocation of the Probability Mass in Knowledge Distillation via α-β-Divergence. | Guanghui Wang, Zhiyong Yang, Zitai Wang, Shi Wang, Qianqian Xu, Qingming Huang |
| 2025 | Multi-Session Budget Optimization for Forward Auction-based Federated Learning. | Xiaoli Tang, Han Yu, Zengxiang Li, Xiaoxiao Li |
| 2025 | Learning Mean Field Control on Sparse Graphs. | Christian Fabian, Kai Cui, Heinz Koeppl |
| 2025 | Splitting & Integrating: Out-of-Distribution Detection via Adversarial Gradient Attribution. | Jiayu Zhang, Xinyi Wang, Zhibo Jin, Zhiyu Zhu, Jianlong Zhou, Fang Chen, Huaming Chen |
| 2025 | Retrieval Augmented Time Series Forecasting. | Sungwon Han, Seungeon Lee, Meeyoung Cha, Sercan . Arik, Jinsung Yoon |
| 2025 | Automatic Reward Shaping from Confounded Offline Data. | Mingxuan Li, Junzhe Zhang, Elias Bareinboim |
| 2025 | Outlier-Aware Post-Training Quantization for Discrete Graph Diffusion Models. | Zheng Gong, Ying Sun |
| 2025 | DLP: Dynamic Layerwise Pruning in Large Language Models. | Yuli Chen, Bo Cheng, Jiale Han, Yingying Zhang, Yingting Li, Shuhao Zhang |
| 2025 | Gaussian Mixture Flow Matching Models. | Hansheng Chen, Kai Zhang, Hao Tan, Zexiang Xu, Fujun Luan, Leonidas J. Guibas, Gordon Wetzstein, Sai Bi |
| 2025 | Self-supervised Masked Graph Autoencoder via Structure-aware Curriculum. | Haoyang Li, Xin Wang, Zeyang Zhang, Zongyuan Wu, Linxin Xiao, Wenwu Zhu |
| 2025 | On Path to Multimodal Generalist: General-Level and General-Bench. | Hao Fei, Yuan Zhou, Juncheng Li, Xiangtai Li, Qingshan Xu, Bobo Li, Shengqiong Wu, Yaoting Wang, Junbao Zhou, Jiahao Meng, Qingyu Shi, Zhiyuan Zhou, Liangtao Shi, Minghe Gao, Daoan Zhang, Zhiqi Ge, Siliang Tang, Kaihang Pan, Yaobo Ye, Haobo Yuan, Tao Zhang, Weiming Wu, Tianjie Ju, Zixiang Meng, Shilin Xu, Liyu Jia, Wentao Hu, Meng Luo, Jiebo Luo, Tat-Seng Chua, Shuicheng Yan, Hanwang Zhang |
| 2025 | Causality Inspired Federated Learning for OOD Generalization. | Jiayuan Zhang, Xuefeng Liu, Jianwei Niu, Shaojie Tang, Haotian Yang, Xinghao Wu |
| 2025 | Surrogate Prompt Learning: Towards Efficient and Diverse Prompt Learning for Vision-Language Models. | Liangchen Liu, Nannan Wang, Xi Yang, Xinbo Gao, Tongliang Liu |
| 2025 | Dataflow-Guided Neuro-Symbolic Language Models for Type Inference. | Ge Li, Yao Wan, Hongyu Zhang, Zhou Zhao, Wenbin Jiang, Xuanhua Shi, Hai Jin, Zheng Wang |
| 2024 | Active Statistical Inference. | Tijana Zrnic, Emmanuel J. Cands |
| 2024 | Compositional Few-Shot Class-Incremental Learning. | Yixiong Zou, Shanghang Zhang, Haichen Zhou, Yuhua Li, Ruixuan Li |
| 2024 | BiE: Bi-Exponent Block Floating-Point for Large Language Models Quantization. | Lancheng Zou, Wenqian Zhao, Shuo Yin, Chen Bai, Qi Sun, Bei Yu |
| 2024 | Hybrid2 Neural ODE Causal Modeling and an Application to Glycemic Response. | Bob Junyi Zou, Matthew E. Levine, Dessi P. Zaharieva, Ramesh Johari, Emily B. Fox |
| 2024 | Leveraging Attractor Dynamics in Spatial Navigation for Better Language Parsing. | Xiaolong Zou, Xingxing Cao, Xiaojiao Yang, Bo Hong |
| 2024 | Fool Your (Vision and) Language Model with Embarrassingly Simple Permutations. | Yongshuo Zong, Tingyang Yu, Ruchika Chavhan, Bingchen Zhao, Timothy M. Hospedales |
| 2024 | Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models. | Yongshuo Zong, Ondrej Bohdal, Tingyang Yu, Yongxin Yang, Timothy M. Hospedales |
| 2024 | Emergence of In-Context Reinforcement Learning from Noise Distillation. | Ilya Zisman, Vladislav Kurenkov, Alexander Nikulin, Viacheslav Sinii, Sergey Kolesnikov |
| 2024 | Viewing Transformers Through the Lens of Long Convolutions Layers. | Itamar Zimerman, Lior Wolf |
3,326–3,350 of 17,065← PreviousNext →
Comparable venues
Other A*/A conferences filed under the same field of research.
- A*ICLRInternational Conference on Learning Representations
- AAISTATSInternational Conference on Artificial Intelligence and Statistics
- A*COLTConference on Learning Theory
- APPSNParallel Problem Solving from Nature
- AECML PKDDEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (PKDD and ECML combined from 2008)
- A*NeurIPSAdvances in Neural Information Processing Systems (was NIPS)