English(EN) Near-Optimal Regret for KL-Regularized Multi-Armed Bandits

新研究探讨老虎机算法的近乎最优遗憾界限

作者 PulseAugur 编辑部 · [2 个来源] · 2026-10-21 04:00

arXiv上发表的两篇新研究论文探讨了多臂老虎机算法的进展。第一篇论文对KL-UCB进行了新颖的分析，为KL正则化多臂老虎机实现了近乎最优的遗憾界限。第二篇论文表明，当与数据稀疏技术相结合时，UCB类算法可以为高斯过程老虎机实现近乎最优的遗憾界限。 AI

影响这些论文推进了对老虎机算法的理论理解，可能导致更高效的在线学习系统。

排序理由该集群包含两篇在arXiv上发表的学术论文，详细介绍了机器学习算法的理论进展。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Kaixuan Ji, Qingyue Zhao, Heyang Zhao, Qiwei Di, Quanquan Gu · 2026-10-23 04:00

KL正则化多臂老虎机近乎最优遗憾

arXiv:2603.02155v2 Announce Type: replace Abstract: Recent studies have shown that reinforcement learning with KL-regularized objectives can enjoy faster rates of convergence or logarithmic regret, in contrast to the classical $\sqrt{T}$-type regret in the unregularized setting. …
arXiv cs.LG TIER_1 English(EN) · Shion Takeno, Shogo Iwazaki, Ichiro Takeuchi · 2026-10-21 04:00

通过数据稀疏化实现 UCB 式高斯过程赌徒的近乎最优遗憾界限

arXiv:2610.28136v1 Announce Type: cross Abstract: Gaussian process upper confidence bound (GP-UCB) has been widely studied, though its known regret upper bound is suboptimal. To obtain near-optimal regret upper bounds, existing studies mainly focus on non-adaptive methods, such a…