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.
By Kaixuan Ji, Qingyue Zhao, Heyang Zhao, Qiwei Di, Quanquan Gu
arXiv:2609. 38659v1 Announce Type: cross Abstract: We study multi-armed bandits (MAB) with multiple optimal arms, motivated by the fact that many practical decision making problems admit multiple correct answers.
By Kaixuan Ji, Qiwei Di, Qingyue Zhao, Heyang Zhao, Quanquan Gu
The paper investigates multi‑armed bandits where several arms are optimal. It refines previous sub‑sampling algorithms to achieve a minimax regret of τO((K−A)/√(KA)·√T), improving on earlier bounds. A matching lower bound is provided, showing the rate is nearly optimal, and the authors demonstrate that knowing the number of optimal arms A (within constant factors) is essential for near‑optimal performance.
By Kaixuan Ji, Qiwei Di, Qingyue Zhao, Heyang Zhao, Quanquan Gu
arXiv:2610. 22252v1 Announce Type: new Abstract: We study how to evaluate $K$ target policies simultaneously in stochastic contextual bandits with linear reward structure.
By Spyros Dragazis, Yilei Chen, Aldo Pacchiano
arXiv:2610.01951v1 Announce Type: cross
Abstract: Top-two algorithms are simple and effective for fixed-confidence best-arm identification, but their sharp non-asymptotic behavior is still not well u...
By Nam Nguyen, Tuan Quang Dam
arXiv:2606. 14690v1 Announce Type: new Abstract: We study a \emph{max-risk} objective for active learning in a multi-group mean estimation $d$-armed bandits: a learner adaptively allocates a budget of $T$ samples across $d$ groups to minimize the worst-case uncertainty index $\max_{k\in[d]}\sigma_k^2/n_k$, where $\sigma_k$ is the standard deviation of the distribution of arm $d$, and $n_k$ is the number of times arm $d$ is sampled.
By Abdellah Aznag, Rachel Cummings, Adam N. Elmachtoub