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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

Showing 451460 of 655 papers

TitleStatusHype
Randomized Exploration for Non-Stationary Stochastic Linear BanditsCode0
Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling0
Ordinal Bayesian Optimisation0
Thompson Sampling and Approximate Inference0
Thompson Sampling for Multinomial Logit Contextual BanditsCode0
Bayesian Optimization for Categorical and Category-Specific Continuous InputsCode0
Automatic Ensemble Learning for Online Influence Maximization0
Multi-Agent Thompson Sampling for Bandit Applications with Sparse Neighbourhood StructuresCode0
Information-Theoretic Confidence Bounds for Reinforcement Learning0
Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling0
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