ICML

Mixture-of-Expert Variational Autoencoders for Cross-Modality Embedding of Type Ia Supernova Data

Yunyi Shen, Alexander T. Gagliano

Comments Accepted to the Machine Learning for Astrophysics Workshop co-located at ICML 2025 and selected for a spotlight talk

LLM Data Selection and Utilization via Dynamic Bi-level Optimization

Yang Yu, Kai Han, Hang Zhou, Yehui Tang, Kaiqi Huang, Yunhe Wang, Dacheng Tao

机构 * School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院） ； The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所认知与决策智能复杂系统重点实验室） ； Huawei Noah’s Ark Lab（华为诺亚实验室） ； College of Intelligence and Computing, Tianjin University（天津大学智能与计算学院） ； Nanyang Technological University（南洋理工大学） 纠错

Comments The 42nd International Conference on Machine Learning (ICML 2025)

MPO: An Efficient Post-Processing Framework for Mixing Diverse Preference Alignment

Tianze Wang, Dongnan Gui, Yifan Hu, Shuhang Lin, Linjun Zhang

机构 * Department of Statistics, Rutgers University, New Brunswick, United States ； Department of Computer Science, Rutgers University, New Brunswick, United States ； College of Management of Technology, EPFL, Switzerland ； Department of Computer Science, ETH Zurich, Switzerland 纠错

Comments ICML 2025

TorchAO: PyTorch-Native Training-to-Serving Model Optimization

Andrew Or, Apurva Jain, Daniel Vega-Myhre, Jesse Cai, Charles David Hernandez, Zhenrui Zheng, Driss Guessous, Vasiliy Kuznetsov, Christian Puhrsch, Mark Saroufim, Supriya Rao, Thien Tran, Aleksandar Samardžić

机构 * Meta Platforms Inc.（Meta公司） ； OpenTeams Inc.（OpenTeams公司） 纠错

Comments 5 pages, 3 figures, published in CODEML@ICML25

Journal ref ICML 2025 Workshop on Championing Open-source DEvelopment (CODEML 2025)

Beyond the ATE: Interpretable Modelling of Treatment Effects over Dose and Time

Julianna Piskorz, Krzysztof Kacprzyk, Harry Amad, Mihaela van der Schaar

机构 * Department of Mathematics and Theoretical Physics, University of Cambridge, United Kingdom（数学与理论物理系，剑桥大学，英国） 纠错

Comments Presented at the Actionable Interpretability Workshop at ICML 2025

Global Convergence and Rich Feature Learning in $L$-Layer Infinite-Width Neural Networks under $μ$P Parametrization

Zixiang Chen, Greg Yang, Qingyue Zhao, Quanquan Gu

机构 * Department of Computer Science, University of California, Los Angeles, USA（计算机科学系，加州大学洛杉矶分校） 纠错

Comments 28 pages, 17 figures, 2 tables. In ICML 2025