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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 571–580 of 655 papers

TitleStatusHype
Bayesian bandits: balancing the exploration-exploitation tradeoff via double samplingCode0
Variational inference for the multi-armed contextual banditCode0
Learning to Price with Reference Effects—0
Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors—0
Thompson Sampling Guided Stochastic Searching on the Line for Deceptive Environments with Applications to Root-Finding Problems—0
Reinforcement learning techniques for Outer Loop Link Adaptation in 4G/5G systems—0
Streaming kernel regression with provably adaptive mean, variance, and regularization—0
Counterfactual Data-Fusion for Online Reinforcement Learners—0
Taming Non-stationary Bandits: A Bayesian Approach—0
Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms: A Case with Bounded Regret—0
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