# Open Problem: Model Selection for Contextual Bandits

arXiv: https://arxiv.org/abs/2006.10940 · HTML: https://mlsanity.com/paper/2006.10940/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2020.

## Built on by (most cited first, 12 of 12)

- [Tracking Most Significant Arm Switches in Bandits](https://mlsanity.com/paper/2112.13838/) (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)
- [Pareto Optimal Model Selection in Linear Bandits](https://mlsanity.com/paper/2102.06593/) (2021)
- [Provably Efficient Representation Selection in Low-rank Markov Decision Processes: From Online to Offline RL](https://mlsanity.com/paper/2106.11935/) (2021)
- [Model Selection for Generic Contextual Bandits](https://mlsanity.com/paper/2107.03455/) (2021)
- [Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers](https://mlsanity.com/paper/2110.14804/) (2021)
- [Contextual Thompson Sampling via Generation of Missing Data](https://mlsanity.com/paper/2502.07064/) (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)
- [Model Selection for Generic Reinforcement Learning](https://mlsanity.com/paper/2107.05849/) (2021)
- [Adversarial Bandits against Arbitrary Strategies](https://mlsanity.com/paper/2205.14839/) (2022)

## Abstract (arXiv)

In statistical learning, algorithms for model selection allow the learner to adapt to the complexity of the best hypothesis class in a sequence. We ask whether similar guarantees are possible for contextual bandit learning.