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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 541–550 of 655 papers

TitleStatusHype
Satisficing in Time-Sensitive Bandit Learning—0
Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype—0
Scalable Generalized Linear Bandits: Online Computation and Hashing—0
Scalable Neural Contextual Bandit for Recommender Systems—0
Scalable regret for learning to control network-coupled subsystems with unknown dynamics—0
Scalable Thompson Sampling using Sparse Gaussian Process Models—0
Scalable Thompson Sampling via Optimal Transport—0
Scaling Multi-Armed Bandit Algorithms—0
Screening for an Infectious Disease as a Problem in Stochastic Control—0
Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization—0
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