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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 281–290 of 655 papers

TitleStatusHype
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound FrameworkCode0
Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo RecommendationsCode0
Modeling Human Exploration Through Resource-Rational Reinforcement LearningCode0
Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems—0
IBAC: An Intelligent Dynamic Bandwidth Channel Access Avoiding Outside Warning Range Problem—0
On Dynamic Pricing with Covariates—0
Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization—0
Safe Linear Leveling Bandits—0
Risk and optimal policies in bandit experiments—0
Bayesian Optimization over Permutation SpacesCode1
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