mlsanityData to 7 Oct 2026

Corralling Stochastic Bandit Algorithms

arXiv 2006.09255 ↗First posted 16 Jun 2020cs.LG stat.MLcited by 23

Not on the map. Why?

Abstract (arXiv)

We study the problem of corralling stochastic bandit algorithms, that is combining multiple bandit algorithms designed for a stochastic environment, with the goal of devising a corralling algorithm that performs almost as well as the best base algorithm. We give two general algorithms for this setting, which we show benefit from favorable regret guarantees. We show that the regret of the corralling algorithms is no worse than that of the best algorithm containing the arm with the highest reward, and depends on the gap between the highest reward and other rewards.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jun 2020.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 11; 2022: 6; 2023: 3; 2024: 1; 2025: 2.

23 papers.

Sort:

By area:

  1. 2021A Model Selection Approach for Corruption Robust Reinforcement Learningcited by 45
  2. 2021Leveraging Good Representations in Linear Contextual Banditscited by 29
  3. 2023A Blackbox Approach to Best of Both Worlds in Bandits and Beyondcited by 29
  4. 2021The Pareto Frontier of model selection for general Contextual Banditscited by 24
  5. 2022Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Banditscited by 22
  6. 2022Adaptively Exploiting d-Separators with Causal Banditscited by 18
  7. 2022Stochastic Rising Banditscited by 18
  8. 2021Thompson Sampling with a Mixture Priorcited by 15
  9. 2021Pareto Optimal Model Selection in Linear Banditscited by 14
  10. 2021Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structurecited by 14
  11. 2022Best of Both Worlds Model Selectioncited by 11
  12. 2023Data-Driven Online Model Selection With Regret Guaranteescited by 11
11 more
  1. 2021Model Selection for Generic Contextual Banditscited by 7
  2. 2022Leveraging Initial Hints for Free in Stochastic Linear Banditscited by 7
  3. 2025Offline-to-online hyperparameter transfer for stochastic banditscited by 6
  4. 2021Universal and data-adaptive algorithms for model selection in linear contextual banditscited by 5
  5. 2023Adaptation to Misspecified Kernel Regularity in Kernelised Banditscited by 5
  6. 2021Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspectivecited by 4
  7. 2021Deep Synoptic Monte Carlo Planning in Reconnaissance Blind Chesscited by 4
  8. 2024Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginalscited by 2
  9. 2021Model Selection for Generic Reinforcement Learningcited by 1
  10. 2022Model Selection in Reinforcement Learning with General Function Approximationscited by 1
  11. 2025Algorithm Design and Stronger Guarantees for the Improving Multi-Armed Bandits Problemcited by 1