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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 261–270 of 655 papers

TitleStatusHype
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
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian ProcessesCode0
Partial Likelihood Thompson Sampling—0
Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework—0
Thompson Sampling with Unrestricted Delays—0
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