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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

Showing 1120 of 655 papers

TitleStatusHype
Neural Exploitation and Exploration of Contextual BanditsCode1
Approximate Thompson Sampling via Epistemic Neural NetworksCode1
Sample-Then-Optimize Batch Neural Thompson SamplingCode1
Langevin Monte Carlo for Contextual BanditsCode1
Bayesian Optimization over Permutation SpacesCode1
EE-Net: Exploitation-Exploration Neural Networks in Contextual BanditsCode1
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep NetworksCode1
Dynamic Slate Recommendation with Gated Recurrent Units and Thompson SamplingCode1
An empirical evaluation of active inference in multi-armed banditsCode1
Mercer Features for Efficient Combinatorial Bayesian OptimizationCode1
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