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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 121–130 of 655 papers

TitleStatusHype
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling—0
Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies—0
Adaptive Sensor Placement for Continuous Spaces—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
A Note on Information-Directed Sampling and Thompson Sampling—0
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits—0
Adaptive Operator Selection Based on Dynamic Thompson Sampling for MOEA/D—0
A Quantile-based Approach for Hyperparameter Transfer Learning—0
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