English(EN) Towards More Efficient, Robust, Instance-adaptive, and Generalizable Sequential Decision making

博士研究聚焦鲁棒和可泛化的序列决策算法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

王志勇的博士研究专注于开发更高效、鲁棒且可泛化的算法，用于不确定性下的序列决策。该研究特别针对强化学习和多臂老虎机问题，旨在解决当前方法依赖理想化模型并可能在模型错误指定或对抗性扰动的现实场景中失效的局限性。该工作试图通过考虑实例依赖的因素和增强对新环境的泛化能力来提高性能。 AI

影响为更具适应性和可靠性的AI决策系统奠定了理论基础。

排序理由该条目是一篇学术论文，详细介绍了关于序列决策算法的博士研究。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Zhiyong Wang · 2026-10-02 04:00

迈向更高效、更鲁棒、实例自适应和可泛化的序列决策

arXiv:2504.09192v5 Announce Type: replace Abstract: The primary goal of my Ph.D. study is to develop provably efficient and practical algorithms for data-driven sequential decision-making under uncertainty. My work focuses on reinforcement learning (RL), multi-armed bandits, and …