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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 601–625 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
Asynchronous Multi Agent Active Search—0
Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems—0
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning—0
Automatic Ensemble Learning for Online Influence Maximization—0
AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning—0
Bag of Policies for Distributional Deep Exploration—0
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration—0
Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control—0
Bandit Convex Optimization: sqrtT Regret in One Dimension—0
Bandit Learning for Diversified Interactive Recommendation—0
Bandit Models of Human Behavior: Reward Processing in Mental Disorders—0
Bandit Policies for Reliable Cellular Network Handovers in Extreme Mobility—0
Bandits Under The Influence (Extended Version)—0
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization—0
Batch Bayesian Optimization for Replicable Experimental Design—0
Batched Thompson Sampling—0
Batched Thompson Sampling for Multi-Armed Bandits—0
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits—0
Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies—0
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program—0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
Bayesian-Guided Generation of Synthetic Microbiomes with Minimized Pathogenicity—0
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