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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 276–300 of 655 papers

TitleStatusHype
Thompson Sampling on Asymmetric α-Stable Bandits—0
Regenerative Particle Thompson Sampling—0
Multi-Agent Active Search using Detection and Location Uncertainty—0
An Analysis of Ensemble Sampling—0
Partial Likelihood Thompson Sampling—0
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian ProcessesCode0
Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework—0
Thompson Sampling with Unrestricted Delays—0
Double Thompson Sampling in Finite stochastic Games—0
Adaptive Experimentation in the Presence of Exogenous Nonstationary Variation—0
Fast online inference for nonlinear contextual bandit based on Generative Adversarial Network—0
Synthetically Controlled Bandits—0
Remote Contextual Bandits—0
Fourier Representations for Black-Box Optimization over Categorical Variables—0
On learning Whittle index policy for restless bandits with scalable regret—0
Bayesian Non-stationary Linear Bandits for Large-Scale Recommender SystemsCode0
Tsetlin Machine for Solving Contextual Bandit ProblemsCode0
Deep Hierarchy in Bandits—0
Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo RecommendationsCode0
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound FrameworkCode0
Modeling Human Exploration Through Resource-Rational Reinforcement LearningCode0
Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems—0
IBAC: An Intelligent Dynamic Bandwidth Channel Access Avoiding Outside Warning Range Problem—0
On Dynamic Pricing with Covariates—0
Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization—0
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