TMLR

Tree Ensembles for Contextual Bandits

基于树集成的上下文老虎机

Hannes Nilsson, Rikard Johansson, Niklas Åkerblom, Morteza Haghir Chehreghani

机构 * Chalmers University of Technology and University of Gothenburg（查尔姆斯理工大学和哥德堡大学） ； Volvo Car Corporation（沃尔沃汽车公司） 纠错

AI总结 本文提出基于树集成的上下文老虎机框架，通过改进不确定性估计方法，在减少遗憾和提升计算效率方面优于传统方法。

Comments The first two authors contributed equally to this work

Journal ref Transactions on Machine Learning Research (TMLR), 2024, https://openreview.net/forum?id=59DCkSGw8S, GitHub: https://github.com/HannesNilsson/tree_ensemble_bandits

Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review

可解释人工智能中的隐私风险与保护方法：一项综述

Sonal Allana, Mohan Kankanhalli, Rozita Dara

AI总结 本文通过综述现有文献，探讨了可解释人工智能中隐私风险与保护方法，分析了隐私与可解释性之间的冲突及平衡策略。

Comments Published in Transactions on Machine Learning Research: https://openreview.net/forum?id=q9nykJfzku

Journal ref Transactions on Machine Learning Research, 10/2025, ISSN=2835-8856

SETS: Leveraging Self-Verification and Self-Correction for Improved Test-Time Scaling

SETS：利用自我验证和自我校正以提高测试时间扩展

Jiefeng Chen, Jie Ren, Xinyun Chen, Chengrun Yang, Ruoxi Sun, Jinsung Yoon, Sercan Ö Arık

机构 * Google Cloud AI Research（谷歌云人工智能研究） 纠错

AI总结 SETS通过结合并行与顺序技术，利用LLMs的自我改进能力，在无需模型训练的情况下提升测试时间扩展性能。

Comments Published in Transactions on Machine Learning Research (11/2025)