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 541–550 of 655 papers

TitleStatusHype
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic ProgrammingCode0
New Insights into Bootstrapping for Bandits—0
Analysis of Thompson Sampling for Graphical Bandits Without the Graphs—0
PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits—0
Profitable Bandits—0
Thompson Sampling for Combinatorial Semi-Bandits—0
Active Reinforcement Learning with Monte-Carlo Tree Search—0
Satisficing in Time-Sensitive Bandit Learning—0
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson SamplingCode0
Efficient Exploration through Bayesian Deep Q-NetworksCode0
Show:102550
← PrevPage 55 of 66Next →

No leaderboard results yet.