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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 461–470 of 655 papers

TitleStatusHype
Incentivized Exploration for Multi-Armed Bandits under Reward Drift—0
Safe Linear Thompson Sampling with Side Information—0
On Batch Bayesian Optimization—0
On Online Learning in Kernelized Markov Decision Processes—0
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints—0
Thompson Sampling via Local UncertaintyCode0
Fixed-Confidence Guarantees for Bayesian Best-Arm Identification—0
Thompson Sampling in Non-Episodic Restless Bandits—0
Old Dog Learns New Tricks: Randomized UCB for Bandit ProblemsCode0
Regret Analysis of Bandit Problems with Causal Background Knowledge—0
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