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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 11–20 of 655 papers

TitleStatusHype
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural ProcessesCode1
Approximate Thompson Sampling via Epistemic Neural NetworksCode1
A Tutorial on Thompson SamplingCode1
Neural Thompson SamplingCode1
Dynamic Slate Recommendation with Gated Recurrent Units and Thompson SamplingCode1
Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood SearchCode1
Batched Bayesian optimization by maximizing the probability of including the optimumCode1
Bayesian Optimization over Permutation SpacesCode1
EE-Net: Exploitation-Exploration Neural Networks in Contextual BanditsCode1
Mercer Features for Efficient Combinatorial Bayesian OptimizationCode1
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