mlsanityData to 7 Oct 2026

Open Problem: Model Selection for Contextual Bandits

arXiv 2006.10940 ↗First posted 19 Jun 2020cs.LG stat.MLcited by 12

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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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jun 2020.

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Citing papers per year: 2021: 9; 2022: 2; 2023: 0; 2024: 0; 2025: 1.

12 papers.

  1. 2021Tracking Most Significant Arm Switches in Banditscited by 27
  2. 2021The Pareto Frontier of model selection for general Contextual Banditscited by 24
  3. 2022Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Banditscited by 22
  4. 2021Pareto Optimal Model Selection in Linear Banditscited by 14
  5. 2021Provably Efficient Representation Selection in Low-rank Markov Decision Processes: From Online to Offline RLcited by 12
  6. 2021Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizerscited by 8
  7. 2021Model Selection for Generic Contextual Banditscited by 7
  8. 2021Universal and data-adaptive algorithms for model selection in linear contextual banditscited by 5
  9. 2025Contextual Thompson Sampling via Generation of Missing Datacited by 5
  10. 2021Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspectivecited by 4
  11. 2021Model Selection for Generic Reinforcement Learningcited by 1
  12. 2022Adversarial Bandits against Arbitrary Strategiescited by 1