# Corralling Stochastic Bandit Algorithms

arXiv: https://arxiv.org/abs/2006.09255 · HTML: https://mlsanity.com/paper/2006.09255/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 23 of 23)

- [A Model Selection Approach for Corruption Robust Reinforcement Learning](https://mlsanity.com/paper/2110.03580/) (2021)
- [A Blackbox Approach to Best of Both Worlds in Bandits and Beyond](https://mlsanity.com/paper/2302.09739/) (2023)
- [Leveraging Good Representations in Linear Contextual Bandits](https://mlsanity.com/paper/2104.03781/) (2021)
- [The Pareto Frontier of model selection for general Contextual Bandits](https://mlsanity.com/paper/2110.13282/) (2021)
- [Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits](https://mlsanity.com/paper/2202.06151/) (2022)
- [Adaptively Exploiting d-Separators with Causal Bandits](https://mlsanity.com/paper/2202.05100/) (2022)
- [Stochastic Rising Bandits](https://mlsanity.com/paper/2212.03798/) (2022)
- [Thompson Sampling with a Mixture Prior](https://mlsanity.com/paper/2106.05608/) (2021)
- [Pareto Optimal Model Selection in Linear Bandits](https://mlsanity.com/paper/2102.06593/) (2021)
- [Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure](https://mlsanity.com/paper/2111.00781/) (2021)
- [Best of Both Worlds Model Selection](https://mlsanity.com/paper/2206.14912/) (2022)
- [Data-Driven Online Model Selection With Regret Guarantees](https://mlsanity.com/paper/2306.02869/) (2023)
- [Model Selection for Generic Contextual Bandits](https://mlsanity.com/paper/2107.03455/) (2021)
- [Leveraging Initial Hints for Free in Stochastic Linear Bandits](https://mlsanity.com/paper/2203.04274/) (2022)
- [Offline-to-online hyperparameter transfer for stochastic bandits](https://mlsanity.com/paper/2501.02926/) (2025)
- [Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspective](https://mlsanity.com/paper/2106.06483/) (2021)
- [Universal and data-adaptive algorithms for model selection in linear contextual bandits](https://mlsanity.com/paper/2111.04688/) (2021)
- [Adaptation to Misspecified Kernel Regularity in Kernelised Bandits](https://mlsanity.com/paper/2304.13830/) (2023)
- [Deep Synoptic Monte Carlo Planning in Reconnaissance Blind Chess](https://mlsanity.com/paper/2110.01810/) (2021)
- [Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals](https://mlsanity.com/paper/2407.00950/) (2024)
- [Algorithm Design and Stronger Guarantees for the Improving Multi-Armed Bandits Problem](https://mlsanity.com/paper/2511.10619/) (2025)
- [Model Selection for Generic Reinforcement Learning](https://mlsanity.com/paper/2107.05849/) (2021)
- [Model Selection in Reinforcement Learning with General Function Approximations](https://mlsanity.com/paper/2207.02992/) (2022)

## 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.