arXiv Machine Learning June 19, 2026 By Asaf Cassel, Aviv Rosenberg

Quantile of Means: A Bonus-Free Ensemble Method for Minimax Optimal Reinforcement Learning

Read the original on arXiv Machine Learning →

arXiv:2606. 20107v1 Announce Type: new Abstract: Optimal Reinforcement Learning (RL) algorithms typically rely on carefully constructed count-based uncertainty estimates to drive exploration.

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reinforcement-learning

arXiv Machine Learning

Jun 2

Finite-Time Regret Analysis of Retry-Aware Bandits

arXiv:2605. 20854v2 Announce Type: replace Abstract: We study a stochastic bandit algorithm motivated by retry-aware objectives that value the best outcome among multiple attempts, such as pass@$k$ and max@$k$.

By Bingkui Tong, Junpei Komiyama, Soichiro Nishimori, Paavo Parmas

reinforcement-learning

arXiv Machine Learning

Aug 4

Conformal bandits: bringing statistical validity and reward efficiency under weak arm separability

arXiv:2512. 09850v2 Announce Type: replace Abstract: We introduce Conformal Bandits, a novel framework integrating Conformal Prediction (CP) into bandit problems, a classic paradigm for sequential decision-making under uncertainty.

By Simone Cuonzo, Nina Deliu

reinforcement-learning

arXiv Machine Learning

Jun 9

Online Learning with Recency: Algorithms for Sliding-window Streaming Multi-armed Bandits

arXiv:2606. 08977v1 Announce Type: new Abstract: Motivated by the recency effect in online learning, we study algorithms for single-pass *sliding-window streaming multi-armed bandits (MABs)* in this paper.

By Vladimir Braverman, Chen Wang, Liudeng Wang, Samson Zhou

arXiv Machine Learning

Sep 22

Multi-Armed Bernoulli Bandits via Minimax Single-Arm Stopping

arXiv:2609. 22690v1 Announce Type: new Abstract: We develop an index policy for finite-horizon Bernoulli multi-armed bandits from minimax solutions to single-arm bandit (SAB) problems.

By Huikang Liu, Zhengchao Wang, Daniel Kuhn, Wolfram Wiesemann

reinforcement-learning benchmarks

arXiv Machine Learning

2d ago

Rising Multi-Armed Bandits with Known Horizons

arXiv:2602.10727v3 Announce Type: replace Abstract: Rising Multi-Armed Bandits (RMABs) model sequential decision problems where each arm's expected reward improves with repeated pulls. In such proble...

By Seockbean Song, Chenyu Gan, Youngsik Yoon, Siwei Wang, Wei Chen, Jungseul Ok

reinforcement-learning benchmarks

arXiv Machine Learning

Jun 30

Randomized Exploration for Linear Bandits via Absolute Perturbations

arXiv:2606. 28616v1 Announce Type: new Abstract: In stochastic linear bandits, the canonical Upper Confidence Bound (UCB) algorithm admits a simple frequentist regret analysis but can be computationally demanding, while Thompson Sampling (TS) is computationally attractive yet typically harder to analyze due to its non-optimistic nature.

By Toshinori Kitamura, Shuai Liu, Csaba Szepesv\'ari

One email a morning, machine-written

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Finite-Time Regret Analysis of Retry-Aware Bandits

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Online Learning with Recency: Algorithms for Sliding-window Streaming Multi-armed Bandits

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