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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 301–310 of 655 papers

TitleStatusHype
Variational Bayesian Optimistic Sampling—0
Differentially Private Federated Bayesian Optimization with Distributed Exploration—0
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits—0
Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes—0
Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized RecommendationsCode0
EE-Net: Exploitation-Exploration Neural Networks in Contextual BanditsCode1
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning—0
Batched Thompson Sampling—0
Asymptotic Performance of Thompson Sampling in the Batched Multi-Armed Bandits—0
Regularized-OFU: an efficient algorithm for general contextual bandit with optimization oracles—0
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