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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 171–180 of 655 papers

TitleStatusHype
Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications—0
Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model—0
Contextual Multi-Armed Bandits for Causal Marketing—0
Contextual Thompson Sampling via Generation of Missing Data—0
Convergence Rates of Posterior Distributions in Markov Decision Process—0
Convolutional Monte Carlo Rollouts in Go—0
Cost Aware Asynchronous Multi-Agent Active Search—0
Cost-efficient Knowledge-based Question Answering with Large Language Models—0
Asymptotically Optimal Bandits under Weighted Information—0
An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits—0
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