arXiv Machine Learning August 18, 2026 By Jingxin Zhan, Yuze Han, Zhihua Zhang

Last-Iterate Analyses of FTRL with the 1/2-Tsallis Entropy in Stochastic Bandits

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arXiv:2510. 22819v3 Announce Type: replace Abstract: The convergence analysis of online learning algorithms is central to machine learning theory, where the last-iterate convergence is particularly important, as it captures the learner's actual decisions and describes the evolution of the learning process over time.

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arXiv Machine Learning

Aug 18

Does 1/2-Tsallis-INF Also Work Well for Best-Arm Identification?

arXiv:2608. 15365v1 Announce Type: new Abstract: Regret minimization (RM) and best-arm identification (BAI) are two fundamental objectives in multi-armed bandits.

By Jingxin Zhan, Yuze Han, Zhihua Zhang

diffusion safety

arXiv Machine Learning

Jun 19

Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem

arXiv:2603. 10184v2 Announce Type: replace-cross Abstract: Statistical inference with bandit data presents fundamental challenges owing to adaptive sampling, which violates the independence assumptions underlying classical asymptotic theory.

By Budhaditya Halder, Ishan Sengupta, Koustav Chowdhury, Samya Praharaj, Koulik Khamaru

reinforcement-learning safety

arXiv Machine Learning

Aug 26

An Efficient Minimax-Optimal Algorithm for Adversarial $m$-Set Bandits

arXiv:2608. 12231v2 Announce Type: replace Abstract: We study adversarial combinatorial bandits with $m$-set actions, where at each round the learner selects $m$ out of $d$ items and observes only the aggregate loss of the selected items.

By Francesco Bacchiocchi, Tommaso Cesari, Roberto Colomboni

safety

arXiv Machine Learning

Jun 29

Learning in Markovian bandits with non-observable states and constrained decision epochs

arXiv:2606. 27448v1 Announce Type: new Abstract: This paper studies the problem of regret minimization in Markovian bandits with \emph{non-observable states} and possibly \emph{constrained} decision epochs.

By Thomas Hira, Victor Boone, Urtzi Ayesta, Ina Maria Verloop

reinforcement-learning benchmarks safety

arXiv Machine Learning

Jul 13

Logarithmic High-Probability Regret for Online Convex Optimization with Two-Point Bandit Feedback

arXiv:2603. 25029v4 Announce Type: replace Abstract: We study online convex optimization (OCO) with two-point bandit feedback against a non-anticipating adaptive adversary.

By Haishan Ye

reinforcement-learning safety

arXiv Statistics ML

2d ago

Sharp Non-Asymptotic Analysis of the Penalized Challenger in $\beta$-EB-TCI for Bernoulli Bandits

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

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Does 1/2-Tsallis-INF Also Work Well for Best-Arm Identification?

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