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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 581–590 of 655 papers

TitleStatusHype
A Nonparametric Contextual Bandit with Arm-level Eligibility Control for Customer Service Routing—0
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits—0
A Note on Information-Directed Sampling and Thompson Sampling—0
An Unbiased Data Collection and Content Exploitation/Exploration Strategy for Personalization—0
Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification—0
Approximate information for efficient exploration-exploitation strategies—0
Approximate Thompson Sampling for Learning Linear Quadratic Regulators with O(T) Regret—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
Efficiently Tackling Million-Dimensional Multiobjective Problems: A Direction Sampling and Fine-Tuning Approach—0
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