ICML

PIGDreamer: Privileged Information Guided World Models for Safe Partially Observable Reinforcement Learning

Dongchi Huang, Jiaqi Wang, Yang Li, Chunhe Xia, Tianle Zhang, Kaige Zhang

机构 * School of Computer Science, University of Beihang, Beijing, China（北京航空航天大学计算机学院） ； School of Computer Science, Chinese University of Hong Kong, Hongkong, China（香港中文大学计算机学院） ； JD Explore Academy, Beijing, China（京东探索研究院） ； North Automatic Control Institute, Taiyuan, China（太原北自动控制研究所） 纠错

Comments ICML 2025

Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data

Mohammad Hosseini, Maryam M. Shanechi

机构 * Viterbi School of Engineering, University of Southern California (USC)（维特比工程学院，南加州大学） 纠错

Comments Published at the 42nd International Conference on Machine Learning (ICML) 2025. Code available at: https://github.com/ShanechiLab/SBIND/

Journal ref ICML 2025

Is Pre-training Truly Better Than Meta-Learning?

Brando Miranda, Patrick Yu, Saumya Goyal, Yu-Xiong Wang, Sanmi Koyejo

机构 * Department of Computer Science, Stanford University（斯坦福大学计算机科学系） ； Machine Learning Department, Carnegie Mellon University（卡内基梅隆大学机器学习系） ； Department of Computer Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校计算机科学系） 纠错

Journal ref Proceedings of the 40th International Conference on Machine Learning 2023 DMLR Workshop