methodology-2
Thompson Sampling
3260 papers • 126 benchmarks • 313 datasets
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.
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Most implemented papers
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
Jasper Snoek, G. Tucker, C. Riquelme•Wed Feb 14 2018
This work benchmarks well-established and recently developed methods for approximate posterior sampling combined with Thompson Sampling over a series of contextual bandit problems and finds that many approaches that have been successful in the supervised learning setting underperformed in the sequential decision-making scenario.
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