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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 271–280 of 655 papers

TitleStatusHype
Non-Stationary Bandit Learning via Predictive Sampling—0
Evolutionary Multi-Armed Bandits with Genetic Thompson SamplingCode0
Thompson Sampling for Bandit Learning in Matching MarketsCode0
On Kernelized Multi-Armed Bandits with Constraints—0
Multi-armed bandits for resource efficient, online optimization of language model pre-training: the use case of dynamic maskingCode0
Thompson Sampling on Asymmetric α-Stable Bandits—0
Regenerative Particle Thompson Sampling—0
Multi-Agent Active Search using Detection and Location Uncertainty—0
An Analysis of Ensemble Sampling—0
Partial Likelihood Thompson Sampling—0
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