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Thompson Sampling

Thompson sampling, named after William R. Thompson, is a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists of choosing the action that maximizes the expected reward with respect to a randomly drawn belief.

Papers

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Showing 331–340 of 655 papers

TitleStatusHype
Scalable regret for learning to control network-coupled subsystems with unknown dynamics—0
Batched Thompson Sampling for Multi-Armed Bandits—0
Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models—0
Debiasing Samples from Online Learning Using Bootstrap—0
Adaptively Optimize Content Recommendation Using Multi Armed Bandit Algorithms in E-commerce—0
From Predictions to Decisions: The Importance of Joint Predictive Distributions—0
GuideBoot: Guided Bootstrap for Deep Contextual Bandits—0
No Regrets for Learning the Prior in Bandits—0
Metalearning Linear Bandits by Prior Update—0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
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