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 91–100 of 655 papers

TitleStatusHype
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models—0
Asymptotic Convergence of Thompson Sampling—0
Asymptotic Performance of Thompson Sampling in the Batched Multi-Armed Bandits—0
Aging Bandits: Regret Analysis and Order-Optimal Learning Algorithm for Wireless Networks with Stochastic Arrivals—0
Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification—0
Asynchronous Multi Agent Active Search—0
Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization—0
An Unbiased Data Collection and Content Exploitation/Exploration Strategy for Personalization—0
Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems—0
Adaptive Sensor Placement for Continuous Spaces—0
Show:102550
← PrevPage 10 of 66Next →

No leaderboard results yet.