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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 110 of 655 papers

TitleStatusHype
Sample-Efficient Alignment for LLMsCode4
Dynamic Slate Recommendation with Gated Recurrent Units and Thompson SamplingCode1
A Bayesian Approach to Online PlanningCode1
A Tutorial on Thompson SamplingCode1
Bayesian Optimization over Permutation SpacesCode1
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep NetworksCode1
An empirical evaluation of active inference in multi-armed banditsCode1
Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood SearchCode1
Approximate Thompson Sampling via Epistemic Neural NetworksCode1
Batched Bayesian optimization by maximizing the probability of including the optimumCode1
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