SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 131–140 of 655 papers

TitleStatusHype
Bayesian Optimization for Categorical and Category-Specific Continuous InputsCode0
Practical Bayesian Learning of Neural Networks via Adaptive Optimisation MethodsCode0
Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space—0
Bayesian-Guided Generation of Synthetic Microbiomes with Minimized Pathogenicity—0
An Empirical Evaluation of Thompson Sampling—0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program—0
Adaptive Grey-Box Fuzz-Testing with Thompson Sampling—0
Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies—0
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling—0
Show:102550
← PrevPage 14 of 66Next →

No leaderboard results yet.