新算法改进了带反馈图的对抗性老虎机算法的遗憾界限

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员为对抗性老虎机开发了一种新算法，该算法为具有反馈的图实现了近乎最优的一阶遗憾保证。该算法弥合了现有理论界限之间的差距，提供了 $\widetilde{O}(\sqrt{\alpha L^\star}+\alpha)$ 的预期遗憾界限。该方法将 EXP3 更新方法与受优化探索启发的创新采样规则相结合，并且可以通过几何收敛迭代高效计算。 AI

影响这项研究推进了老虎机算法的理论理解，有望在复杂、不确定的环境中实现更高效的决策。

排序理由该条目是一篇在 arXiv 上发表的学术论文，详细介绍了机器学习中的新算法和理论结果。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Mengxiao Zhang, Yuheng Zhang, Haipeng Luo, Paul Mineiro · 2026-11-02 05:00

Near-Optimal First-Order Regret for Bandits with Feedback Graphs

arXiv:2610.40861v1 Announce Type: cross Abstract: We study first-order regret guarantees for adversarial bandits with a fixed, known directed feedback graph. For strongly observable graphs, [ACBDK15] establishes a minimax regret of $\widetilde{\Theta}(\sqrt{\alpha T})$, where $\a…

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Near-Optimal First-Order Regret for Bandits with Feedback Graphs

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