ICML

CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables

CATS: 通过构建辅助时间序列作为外生变量增强多变量时间序列预测

Jiecheng Lu, Xu Han, Yan Sun, Shihao Yang

机构 * Georgia Institute of Technology（佐治亚理工学院） ； Amazon Web Services（亚马逊网络服务） 纠错

AI总结 CATS通过构建辅助时间序列作为外生变量，有效提升多变量时间序列预测的性能，实现高效且可转移的预测解决方案。

Comments Camera-ready version. Accepted at ICML 2024

Journal ref Proceedings of the Forty-first International Conference on Machine Learning (ICML 2024)

Improved Generalization Bounds for Transductive Learning by Transductive Local Complexity and Its Applications

通过传递学习局部复杂度改进传递学习的泛化界及其应用

Yingzhen Yang

机构 * School of Computing and Augmented Intelligence（计算与增强智能学院） ； Arizona State University（亚利桑那州立大学） 纠错

AI总结 本文通过引入传递局部复杂度，改进了传递学习的泛化界，并在二值类和核学习中取得了新的理论突破。

Comments The ICML 2025 conference version (https://openreview.net/pdf?id=NRVdvg7VMn) is a special case of this paper where the chain length is fixed at 2 (i.e.,$Q=2$, see Def. 5.1), and its main results follow directly from the results here. This paper further provides a nearly optimal excess risk bound for realizable transductive learning and a stronger bound for transductive kernel learning

Self-explaining Neural Network with Concept-based Explanations for ICU Mortality Prediction

具有基于概念的解释的自解释神经网络用于ICU死亡率预测

Sayantan Kumar, Sean C. Yu, Thomas Kannampallil, Zachary Abrams, Andrew Michelson, Philip R. O. Payne

机构 * Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, USA（计算机科学与工程系，华盛顿大学圣路易斯分校） ； Institute for Informatics, Washington University School of Medicine, St. Louis, MO, USA（信息学院，华盛顿大学医学学院） ； Department of Anaesthesiology, Washington University School of Medicine, St. Louis, MO, USA（麻醉学系，华盛顿大学医学学院） ； Department of Pulmonary Critical Care and Medicine, Washington University School of Medicine, St. Louis, MO, USA（呼吸科重症医学系，华盛顿大学医学学院） 纠错

AI总结 本文提出了一种基于概念的自解释神经网络，用于ICU患者死亡率预测，通过联合训练生成解释和预测，提升模型的可解释性与预测性能。

Comments Workshop on Interpretable ML in Healthcare at International Conference on Machine Learning (ICML 2022)

Journal ref BCB '22: Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Article No.: 8, Pages 1 - 9, 2022

DCER: Dual-Stage Compression and Energy-Based Reconstruction

DCER：双阶段压缩与基于能量的重建

Yiwen Wang, Jiahao Qin

机构 * Yiwen Wang（无） ； Jiahao Qin（无） 纠错

AI总结 DCER通过双阶段压缩和基于能量的重建解决多模态融合中的噪声和缺失模态问题，实现鲁棒性提升。

Comments 13 pages, 2 figures, 8 tables. Submitted to ICML 2026. Code will be available on GitHub

Linear Transformers as VAR Models: Aligning Autoregressive Attention Mechanisms with Autoregressive Forecasting

线性变换器作为VAR模型：将自回归注意力机制与自回归预测对齐

Jiecheng Lu, Shihao Yang

机构 * Georgia Institute of Technology（佐治亚理工学院） 纠错

AI总结 本文提出SAMoVAR，一种将Transformer架构与自回归目标对齐的线性变换器变体，通过整合可解释的动态VAR权重，提升时间序列预测的性能和可解释性。

Comments Camera-ready version. Accepted at ICML 2025

Journal ref Proceedings of the Forty-second International Conference on Machine Learning (ICML 2025)

Human Body Restoration with One-Step Diffusion Model and A New Benchmark

一步扩散模型与新基准的人体恢复

Jue Gong, Jingkai Wang, Zheng Chen, Xing Liu, Hong Gu, Yulun Zhang, Xiaokang Yang

机构 * Shanghai Jiao Tong University, China（上海交通大学） ； vivo Mobile Communication Co., Ltd, China（vivo移动通信有限公司） 纠错

AI总结 本文提出了一种一步扩散模型OSDHuman和新基准数据集PERSONA，用于提升人体恢复的视觉质量和定量指标。

Comments 8 pages, 9 figures. Accepted at ICML 2025

WAVE: Weighted Autoregressive Varying Gate for Time Series Forecasting

WAVE：带有自回归和移动平均组件的加权自回归变门机制用于时间序列预测

Jiecheng Lu, Xu Han, Yan Sun, Shihao Yang

机构 * Georgia Institute of Technology（佐治亚理工学院） 纠错

AI总结 WAVE通过整合ARMA结构提升时间序列预测性能，结合自回归和移动平均组件，实现更高效的长程和局部时间模式捕捉。

Comments Camera-ready version. Accepted at ICML 2025

Journal ref Proceedings of the Forty-second International Conference on Machine Learning (ICML 2025)