arXiv Machine Learning June 16, 2026 By Argyrios Gerogiannis, Yu-Han Huang, Subhonmesh Bose, Venugopal V. Veeravalli

DAL: A Practical Prior-Free Black-Box Framework for Piecewise Stationary Bandits

Read the original on arXiv Machine Learning →

arXiv:2501. 19401v5 Announce Type: replace Abstract: We introduce a practical, black-box framework termed Detection Augmented Learning (DAL) for the problem of piecewise stationary bandits without knowledge of the underlying non-stationarity.

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arXiv Machine Learning

Jun 4

Offline-to-Online Learning in Linear Bandits

arXiv:2606. 04305v1 Announce Type: new Abstract: We study online learning with an additional offline dataset in the stochastic linear bandit setting.

By Kushagra Chandak, Toshinori Kitamura, Xiaoqi Tan

reinforcement-learning

arXiv Machine Learning

Jun 2

Adaptive Exploration for Latent-State Bandits

arXiv:2602. 05139v3 Announce Type: replace Abstract: We study bandits whose rewards depend on an unobserved Markov state that evolves independently of the learner's actions.

By Jikai Jin, Kenneth Hung, Sanath Kumar Krishnamurthy, Baoyi Shi, Congshan Zhang

reinforcement-learning safety

arXiv Machine Learning

Jun 24

Flow-Corrected Thompson Sampling for Non-Stationary Contextual Bandits

arXiv:2606. 23933v1 Announce Type: cross Abstract: We study non-stationary linear contextual bandits where the reward model drifts over time, rendering classical contextual bandit algorithms brittle because historical data becomes systematically biased.

By AmirHossein Naghdi, Ali Baheri

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 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

Aug 12

Weighted Sequential Bayesian Inference for Non-Stationary Linear Contextual Bandits

arXiv:2307. 03587v4 Announce Type: replace Abstract: In non-stationary linear contextual bandits, existing efficient algorithms typically rely on the Weighted Regularized Least-Squares (WRLS) estimator.

By Nicklas Werge, Yi-Shan Wu, Abdullah Akg\"ul, Melih Kandemir

benchmarks

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Offline-to-Online Learning in Linear Bandits

Adaptive Exploration for Latent-State Bandits

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Rising Multi-Armed Bandits with Known Horizons

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