arXiv Machine Learning June 24, 2026 By AmirHossein Naghdi, Ali Baheri

Flow-Corrected Thompson Sampling for Non-Stationary Contextual Bandits

Read the original on arXiv Machine Learning →

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.

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

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 Machine Learning

Jul 21

Periodic Bootstrap Thompson Sampling For Periodically Non-Stationary Bandit Problems

arXiv:2607. 16986v1 Announce Type: new Abstract: This paper introduces Periodic Bootstrap Thompson Sampling (PBTS), an innovative extension of the classic Thompson Sampling (TS) algorithm tailored for bandit problems with periodic non-stationarity.

By Boning Shao

rag reinforcement-learning

arXiv Machine Learning

Jun 2

A Direct Approach for Handling Contextual Bandits with Latent State Dynamics

arXiv:2604. 08149v2 Announce Type: replace Abstract: We consider a linear contextual bandit model where contexts and rewards are governed by a finite hidden Markov chain.

By Zhen Li (LMO, CELESTE, HEC Paris), Gilles Stoltz (LMO, CELESTE, HEC Paris)

reinforcement-learning

arXiv Machine Learning

Jun 9

Adaptive Prior Selection in Gaussian Process Bandits with Thompson Sampling

arXiv:2502. 01226v4 Announce Type: replace Abstract: Gaussian process (GP) bandits provide a powerful framework for performing blackbox optimization of unknown functions.

By Jack Sandberg, Morteza Haghir Chehreghani

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

Jun 2

Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates

arXiv:2606. 00984v1 Announce Type: cross Abstract: We study linear contextual bandits under rare parameter updates: the learner may incorporate reward feedback into its parameter estimate only at a small number of update times, while still observing contexts online and selecting actions sequentially.

By Sanghoon Yu, Min-hwan Oh

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Weighted Sequential Bayesian Inference for Non-Stationary Linear Contextual Bandits

Periodic Bootstrap Thompson Sampling For Periodically Non-Stationary Bandit Problems

A Direct Approach for Handling Contextual Bandits with Latent State Dynamics

Adaptive Prior Selection in Gaussian Process Bandits with Thompson Sampling

Finite-Time Regret Analysis of Retry-Aware Bandits

Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates