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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 71–80 of 655 papers

TitleStatusHype
A resource-constrained stochastic scheduling algorithm for homeless street outreach and gleaning edible food—0
Adaptive Experimentation in the Presence of Exogenous Nonstationary Variation—0
Approximate Thompson Sampling for Learning Linear Quadratic Regulators with O(T) Regret—0
Approximate information for efficient exploration-exploitation strategies—0
Fast Change Identification in Multi-Play Bandits and its Applications in Wireless Networks—0
A Bayesian Choice Model for Eliminating Feedback Loops—0
Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification—0
A Practical Method for Solving Contextual Bandit Problems Using Decision Trees—0
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning—0
An Unbiased Data Collection and Content Exploitation/Exploration Strategy for Personalization—0
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