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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 251–275 of 655 papers

TitleStatusHype
From Predictions to Decisions: The Importance of Joint Predictive Distributions—0
Evaluation of Explore-Exploit Policies in Multi-result Ranking Systems—0
Convergence Rates of Posterior Distributions in Markov Decision Process—0
Expected Improvement-based Contextual Bandits—0
A study of Thompson Sampling with Parameter h—0
A Formal Solution to the Grain of Truth Problem—0
AdaptEx: A Self-Service Contextual Bandit Platform—0
Contextual Thompson Sampling via Generation of Missing Data—0
Contextual Multi-Armed Bandits for Causal Marketing—0
A Simple and Optimal Policy Design with Safety against Heavy-Tailed Risk for Stochastic Bandits—0
Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model—0
Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications—0
A sequential Monte Carlo approach to Thompson sampling for Bayesian optimization—0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation—0
Contextual Bandits with Non-Stationary Correlated Rewards for User Association in MmWave Vehicular Networks—0
Contextual Bandits for Advertising Budget Allocation—0
A resource-constrained stochastic scheduling algorithm for homeless street outreach and gleaning edible food—0
Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling—0
Context Attribution with Multi-Armed Bandit Optimization—0
A Reliability-aware Multi-armed Bandit Approach to Learn and Select Users in Demand Response—0
Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization—0
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling—0
Context Attentive Bandits: Contextual Bandit with Restricted Context—0
A relaxed technical assumption for posterior sampling-based reinforcement learning for control of unknown linear systems—0
Constrained Thompson Sampling for Wireless Link Optimization—0
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