arXiv Statistics ML

Block Optimism for Nonstationary Bandits with Latent Linear Dynamics

arXiv Machine Learning

Aug 18

Generalized Linear Bandits with Memory

arXiv:2608. 15848v1 Announce Type: cross Abstract: We study generalized linear bandits with memory, an endogenous non-stationary setting in which rewards depend on past actions through a finite memory matrix.

By Heesang Ann, Hyunjun Choi, Taehyun Hwang, Younghoon Shin, Haeju Cheong, Min-hwan Oh

arXiv Machine Learning

Jul 7

Dynamic Regret for Non-Stationary Linear Bandits via Misspecification Reductions

arXiv:2607. 02891v1 Announce Type: new Abstract: Many online decision-making problems involve both round-specific feasible actions and drifting reward models: eligible ad impressions, feasible prices, and available treatments can change over time, while user preferences, demand curves, and patient responses may evolve.

By Zihao Hu, Yuan Yao, Jiheng Zhang, Zhengyuan Zhou

reinforcement-learning benchmarks

arXiv Machine Learning

Jun 30

Optimal Regret for Single Index Bandits

arXiv:2605. 09454v2 Announce Type: replace-cross Abstract: We study the $\textit{single-index bandit}$ problem, where rewards depend on an unknown one-dimensional projection of high-dimensional contexts through an unknown reward function.

By Devdan Dey, Sujoy Bhore, Avishek Ghosh

reinforcement-learning

arXiv Machine Learning

Jun 10

Replicable Bandits with UCB based Exploration

arXiv:2604. 20024v2 Announce Type: replace Abstract: We study replicable algorithms for stochastic multi-armed bandits (MAB) and linear bandits with UCB (Upper Confidence Bound) based exploration.

By Rohan Deb, Udaya Ghai, Karan Singh, Arindam Banerjee

reinforcement-learning

arXiv Machine Learning

Aug 4

A Perturbation Approach to Unconstrained Linear Bandits

arXiv:2603. 28201v3 Announce Type: replace Abstract: We revisit the standard perturbation-based approach of Abernethy et al.

By Andrew Jacobsen, Dorian Baudry, Shinji Ito, Nicol\`o Cesa-Bianchi

reinforcement-learning safety

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

arXiv Machine Learning

Jun 29

Learning in Markovian bandits with non-observable states and constrained decision epochs

arXiv:2606. 27448v1 Announce Type: new Abstract: This paper studies the problem of regret minimization in Markovian bandits with \emph{non-observable states} and possibly \emph{constrained} decision epochs.

By Thomas Hira, Victor Boone, Urtzi Ayesta, Ina Maria Verloop

reinforcement-learning benchmarks safety

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

Algorithm for Contextual Queueing Bandits with Rate-Optimal Queue Length Regret

arXiv:2606. 09668v1 Announce Type: new Abstract: Contextual queueing bandits provide a framework for learning to schedule heterogeneous jobs under unknown context-dependent service rates.

By Seoungbin Bae, Dabeen Lee

arXiv Machine Learning

Jul 16

On the Sublinear Regret of Continuous K-Max Bandits

arXiv:2502. 13467v2 Announce Type: replace Abstract: The $K$-Max combinatorial multi-armed bandit problem arises in applications such as recommendation and distributed decision making, where the reward is determined by the maximum outcome among $K$ selected arms.

By Yu Chen, Siwei Wang, Longbo Huang, Wei Chen

reinforcement-learning safety

arXiv Machine Learning

Aug 20

Sharper Regret Bounds for Time-Varying Gaussian Process Bandits with Constant Exploration

arXiv:2608. 18863v1 Announce Type: cross Abstract: We study Bayesian optimization in a time-varying environment where the unknown reward function evolves according to a Gaussian process drift model.

By Matthias Mandl, Hanne Kekkonen

arXiv Statistics ML

Sep 2

Model Predictive Control is almost Optimal for Heterogeneous Restless Multi-armed Bandits

arXiv:2511.08097v2 Announce Type: replace-cross Abstract: We consider a general infinite horizon Heterogeneous Restless multi-armed Bandit (RMAB). Heterogeneity is a fundamental problem for many real...

By Dheeraj Narasimha, Nicolas Gast

reinforcement-learning

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