arXiv Machine Learning

Rising Multi-Armed Bandits with Known Horizons

arXiv Machine Learning

Jun 9

Multi-Armed Bandits with Arriving Arms: Sequential Screening, Dynamic Regret, and Sublinear Guarantees

arXiv:2606. 09002v1 Announce Type: cross Abstract: We study a stochastic multi-armed bandit problem in which the set of available arms expands over time.

By Deqi Zheng, Xiaoyang Xu, Yuhong Yang

reinforcement-learning benchmarks

arXiv Machine Learning

Sep 22

Multi-Armed Bernoulli Bandits via Minimax Single-Arm Stopping

arXiv:2609. 22690v1 Announce Type: new Abstract: We develop an index policy for finite-horizon Bernoulli multi-armed bandits from minimax solutions to single-arm bandit (SAB) problems.

By Huikang Liu, Zhengchao Wang, Daniel Kuhn, Wolfram Wiesemann

reinforcement-learning benchmarks

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

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 4

Conformal bandits: bringing statistical validity and reward efficiency under weak arm separability

arXiv:2512. 09850v2 Announce Type: replace Abstract: We introduce Conformal Bandits, a novel framework integrating Conformal Prediction (CP) into bandit problems, a classic paradigm for sequential decision-making under uncertainty.

By Simone Cuonzo, Nina Deliu

reinforcement-learning

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

4d ago

Learning When to Update: A Near-Optimal Timing Bandit Approach

arXiv:2609.37932v1 Announce Type: new Abstract: Systems operating in dynamic environments require timely updates to sustain performance. For resource-intensive systems such as machine learning models...

By Qiulin Lin, Junyan Su, Liyuan Wang, Minghua Chen

reinforcement-learning

arXiv Machine Learning

Aug 3

The Greedy Advantage in Finite-Horizon Bandits

arXiv:2607. 29375v1 Announce Type: cross Abstract: Organizations increasingly rely on sequential experimentation to improve decision-making.

By Kai Zhou, Michael Lingzhi Li, Kai Wang

reinforcement-learning benchmarks

arXiv Machine Learning

Jun 16

Best Arm Identification with Minimal Regret

arXiv:2409. 18909v2 Announce Type: replace Abstract: Motivated by real-world applications that necessitate responsible experimentation, we introduce the problem of best arm identification (BAI) with minimal regret.

By Junwen Yang, Vincent Y. F. Tan, Tianyuan Jin

agents reinforcement-learning

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 2

Tree-Guided Identify-Then-Exploit: A Unified Framework of Best Arm Identification and Regret Minimization for Dueling Bandits

arXiv:2606. 01799v1 Announce Type: new Abstract: We study $N$-armed stochastic dueling bandits under the Condorcet-winner assumption, where three widely adopted objectives are considered: best-arm identification (BAI), weak regret, and strong regret.

By Pu Wang, Yao-Xiang Ding

arXiv Machine Learning

Sep 23

Sharp Characterization of Bias in Post-Bandit Inference

arXiv:2608.01069v2 Announce Type: replace Abstract: Bandit algorithms generate data for downstream inference, but adaptive sampling biases post-bandit sample means. We analyze this bias for stable in...

By Lisu Wang, Yilun Chen, Jiaqi Lu

reinforcement-learning safety

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