arXiv Machine Learning July 8, 2026 By Eliabelle Mauduit, Elo\"ise Berthier, Andrea Simonetto

No-Regret Gaussian Process Optimization of Time-Varying Functions

Read the original on arXiv Machine Learning →

arXiv:2512. 00517v3 Announce Type: replace-cross Abstract: Sequential optimization of black-box functions from noisy evaluations has been widely studied, with Gaussian Process bandit algorithms such as GP-UCB guaranteeing no-regret in stationary settings.

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

reinforcement-learning

arXiv Machine Learning

1d ago

Improved Regret Analysis for Parallel Gaussian Process Bandit Optimization

arXiv:2608. 16492v1 Announce Type: cross Abstract: This paper studies the regret analysis for parallel Gaussian process (GP) bandit optimization.

By Shion Takeno, Shogo Iwazaki

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

Jun 11

On Regret Bounds of Thompson Sampling for Bayesian Optimization

arXiv:2603. 09276v2 Announce Type: replace-cross Abstract: We study a widely used Bayesian optimization method, Gaussian process Thompson sampling (GP-TS), under the assumption that the objective function is a sample path from a GP.

By Shion Takeno, Shogo Iwazaki

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

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 9

Nonlinear Bandit

arXiv:2607. 07304v1 Announce Type: new Abstract: In this paper we first study the problem of generalized linear bandit (GLB) under heavy-tailed noise.

By Tianshuo Zheng, Ting Wu, Zhi-Hua Zhou, Keqin Liu

reinforcement-learning

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Improved Regret Analysis for Parallel Gaussian Process Bandit Optimization

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