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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 621–630 of 655 papers

TitleStatusHype
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits—0
Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies—0
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program—0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
Bayesian-Guided Generation of Synthetic Microbiomes with Minimized Pathogenicity—0
Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space—0
Bayesian learning of the optimal action-value function in a Markov decision process—0
Bayesian Mixture Modelling and Inference based Thompson Sampling in Monte-Carlo Tree Search—0
Bayesian Optimization-Based Beam Alignment for MmWave MIMO Communication Systems—0
Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?—0
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