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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 371–380 of 655 papers

TitleStatusHype
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling—0
Horde of Bandits using Gaussian Markov Random Fields—0
Human collective intelligence as distributed Bayesian inference—0
Hypermodels for Exploration—0
IBAC: An Intelligent Dynamic Bandwidth Channel Access Avoiding Outside Warning Range Problem—0
Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning—0
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems—0
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration—0
Improving Reward-Conditioned Policies for Multi-Armed Bandits using Normalized Weight Functions—0
Improving sample efficiency of high dimensional Bayesian optimization with MCMC—0
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