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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.

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Showing 451–475 of 655 papers

TitleStatusHype
Thompson Sampling Achieves O(T) Regret in Linear Quadratic Control—0
Thompson Sampling with Approximate Inference—0
Thompson Sampling and Approximate Inference—0
Analysis of Thompson Sampling for Controlling Unknown Linear Diffusion Processes—0
Thompson Sampling for 1-Dimensional Exponential Family Bandits—0
Thompson Sampling for Adversarial Bit Prediction—0
Thompson Sampling for Bandits with Clustered Arms—0
Thompson Sampling for Budgeted Multi-armed Bandits—0
Thompson Sampling Algorithms for Cascading Bandits—0
Thompson Sampling for Combinatorial Network Optimization in Unknown Environments—0
Thompson Sampling for (Combinatorial) Pure Exploration—0
Thompson Sampling for Combinatorial Semi-Bandits—0
Thompson Sampling for Combinatorial Semi-bandits with Sleeping Arms and Long-Term Fairness Constraints—0
Thompson Sampling for Complex Bandit Problems—0
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints—0
Thompson Sampling for Dynamic Pricing—0
Thompson Sampling for Gaussian Entropic Risk Bandits—0
Thompson sampling for improved exploration in GFlowNets—0
Thompson Sampling for Infinite-Horizon Discounted Decision Processes—0
Thompson Sampling for Learning Parameterized Markov Decision Processes—0
Thompson Sampling for Linear Bandit Problems with Normal-Gamma Priors—0
Thompson Sampling for Linear-Quadratic Control Problems—0
Thompson sampling for linear quadratic mean-field teams—0
Thompson Sampling for Noncompliant Bandits—0
Thompson Sampling for Online Learning with Linear Experts—0
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