arXiv Machine Learning August 18, 2026 By Yanjun Han, Zhengqing Zhou, Zihao Hu, Jose Blanchet, Peter W. Glynn, Yinyu Ye, Zhengyuan Zhou

Sequential Batch Learning in Finite-Action Linear Contextual Bandits

Read the original on arXiv Machine Learning →

arXiv:2004. 06321v2 Announce Type: replace Abstract: We study the sequential batch learning problem in linear contextual bandits with finite action sets, where the decision maker is constrained to split incoming individuals into (at most) a fixed number of batches and can only observe outcomes for the individuals within a batch at the batch's end.

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

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

Hugging Face Trending Papers

Jul 15

Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation

We study the contextual combinatorial semi-bandit (CCSB) problem with general reward function approximation. At each round, the learner observes a context, selects a combinatorial action consisting of a subset of basic arms, and receives the reward of each selected arm; the goal is to maximize the cumulative reward over time.

reinforcement-learning benchmarks

arXiv Machine Learning

Jul 16

Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation

arXiv:2607. 13686v1 Announce Type: new Abstract: We study the contextual combinatorial semi-bandit (CCSB) problem with general reward function approximation.

By Hao Qin, Chicheng Zhang

reinforcement-learning benchmarks

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 AI

Jun 17

Online LLM Selection via Constrained Bandits with Time-Varying Demand

arXiv:2606. 17489v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed in edge-cloud inference systems to handle diverse user tasks with heterogeneous accuracy, latency, and cost profiles.

By Yin Huang, Qingsong Liu, Jie Xu

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jun 3

Human-in-the-Loop Contextual Bandits for Short-Term Rental Dynamic Pricing: Structural Equivalence of Historical Warm-Up and Approval-Gated Live Learning

arXiv:2606. 02595v1 Announce Type: new Abstract: Dynamic pricing in short-term rental (STR) markets presents a distinctive challenge for online learning algorithms: pricing decisions carry significant financial risk, operators require explainability, and market feedback is sparse (one booking outcome per listed night).

By Oleg Miroshnichenko

agents reinforcement-learning

Related stories

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

Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation

Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation

A Direct Approach for Handling Contextual Bandits with Latent State Dynamics

Online LLM Selection via Constrained Bandits with Time-Varying Demand

Human-in-the-Loop Contextual Bandits for Short-Term Rental Dynamic Pricing: Structural Equivalence of Historical Warm-Up and Approval-Gated Live Learning