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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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

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

arXiv AI

Jun 9

Bandits for Efficient Experimentation: Adapting to Control Group, Preferences, and Context Drifts

arXiv:2606. 09802v1 Announce Type: cross Abstract: We consider a variant of the linear contextual stochastic multi-armed bandits, where the learner must provide recommendations to a group of users, each having its personalized preference vector, and in the presence of context distributions that are drifting over time.

By Udvas Das, Waris Radji, Debabrota Basu, Odalric-Ambrym Maillard

reinforcement-learning

Related stories

Offline-to-Online Learning in Linear Bandits

Adaptive Exploration for Latent-State Bandits

Flow-Corrected Thompson Sampling for Non-Stationary Contextual Bandits

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

Weighted Sequential Bayesian Inference for Non-Stationary Linear Contextual Bandits

Bandits for Efficient Experimentation: Adapting to Control Group, Preferences, and Context Drifts