Mixed-Effect Thompson Sampling - Archive ouverte HAL
Communication Dans Un Congrès Année : 2023

Mixed-Effect Thompson Sampling

Imad Aouali
  • Fonction : Auteur
Branislav Kveton
  • Fonction : Auteur
Sumeet Katariya
  • Fonction : Auteur

Résumé

A contextual bandit is a popular framework for online learning to act under uncertainty. In practice, the number of actions is huge and their expected rewards are correlated. In this work, we introduce a general framework for capturing such correlations through a mixed-effect model where actions are related through multiple shared effect parameters. To explore efficiently using this structure, we propose Mixed-Effect Thompson Sampling (meTS) and bound its Bayes regret. The regret bound has two terms, one for learning the action parameters and the other for learning the shared effect parameters. The terms reflect the structure of our model and the quality of priors. Our theoretical findings are validated empirically using both synthetic and real-world problems. We also propose numerous extensions of practical interest. While they do not come with guarantees, they perform well empirically and show the generality of the proposed framework.
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Dates et versions

hal-04606080 , version 1 (09-06-2024)

Identifiants

  • HAL Id : hal-04606080 , version 1

Citer

Imad Aouali, Branislav Kveton, Sumeet Katariya. Mixed-Effect Thompson Sampling. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, Apr 2023, Valencia, Spain. pp.2087--2115. ⟨hal-04606080⟩
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