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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 501–510 of 655 papers

TitleStatusHype
Sampling Acquisition Functions for Batch Bayesian Optimization—0
On Multi-Armed Bandit Designs for Dose-Finding Clinical Trials—0
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy CriticsCode0
Meta Dynamic Pricing: Transfer Learning Across Experiments—0
Constrained Thompson Sampling for Wireless Link Optimization—0
Fully Distributed Bayesian Optimization with Stochastic Policies—0
Multi-Armed Bandit Strategies for Non-Stationary Reward Distributions and Delayed Feedback Processes—0
Scalable Thompson Sampling via Optimal Transport—0
Thompson Sampling with Information Relaxation PenaltiesCode0
KLUCB Approach to Copeland Bandits—0
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