arXiv Machine Learning July 7, 2026 By Junpei Komiyama, Shinji Ito, Yuichi Yoshida, Souta Koshino

Replicability is Asymptotically Free in Multi-armed Bandits

Read the original on arXiv Machine Learning →

arXiv:2402. 07391v3 Announce Type: replace-cross Abstract: We consider a replicable stochastic multi-armed bandit algorithm that ensures, with high probability, that the algorithm's sequence of actions is not affected by the randomness inherent in the dataset.

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reinforcement-learning

arXiv Machine Learning

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Replicable Bandits with UCB based Exploration

arXiv:2604. 20024v2 Announce Type: replace Abstract: We study replicable algorithms for stochastic multi-armed bandits (MAB) and linear bandits with UCB (Upper Confidence Bound) based exploration.

By Rohan Deb, Udaya Ghai, Karan Singh, Arindam Banerjee

reinforcement-learning

arXiv Machine Learning

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Regret Analysis of Retry-Based Bandits

arXiv:2605.20854v3 Announce Type: replace Abstract: We provide the first regret analysis of ReMax in stochastic multi-armed bandits. Originally introduced for reinforcement learning, ReMax is motivat...

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reinforcement-learning

arXiv AI

3d ago

Bandits with Multiple Optimal Arms: Minimax Regret and Non-Adaptivit

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arXiv AI

2d ago

Bandits with Multiple Optimal Arms: Minimax Regret and Non-Adaptivity

arXiv:2609.38659v2 Announce Type: replace-cross Abstract: We study multi-armed bandits (MAB) with multiple optimal arms, motivated by the fact that many practical decision making problems admit multi...

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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

arXiv Machine Learning

Jun 16

Best Arm Identification with Minimal Regret

arXiv:2409. 18909v2 Announce Type: replace Abstract: Motivated by real-world applications that necessitate responsible experimentation, we introduce the problem of best arm identification (BAI) with minimal regret.

By Junwen Yang, Vincent Y. F. Tan, Tianyuan Jin

agents reinforcement-learning

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