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 301–310 of 655 papers

TitleStatusHype
Improving sample efficiency of high dimensional Bayesian optimization with MCMC—0
Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments—0
Improving Reward-Conditioned Policies for Multi-Armed Bandits using Normalized Weight Functions—0
Incentivizing Combinatorial Bandit Exploration—0
Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems—0
Incentivizing Exploration with Linear Contexts and Combinatorial Actions—0
Incorporating Behavioral Constraints in Online AI Systems—0
Increasing Students' Engagement to Reminder Emails Through Multi-Armed Bandits—0
Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits—0
Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification—0
Show:102550
← PrevPage 31 of 66Next →

No leaderboard results yet.