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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 141–150 of 655 papers

TitleStatusHype
Better Optimism By Bayes: Adaptive Planning with Rich Models—0
Blind Exploration and Exploitation of Stochastic Experts—0
Bootstrapped Thompson Sampling and Deep Exploration—0
BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings—0
Calibrated Fairness in Bandits—0
A Note on Information-Directed Sampling and Thompson Sampling—0
An Unbiased Data Collection and Content Exploitation/Exploration Strategy for Personalization—0
Causal Bandits without prior knowledge using separating sets—0
Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems—0
Bayesian Quantile and Expectile Optimisation—0
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