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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 326–350 of 655 papers

TitleStatusHype
Efficient Online Learning for Cognitive Radar-Cellular Coexistence via Contextual Thompson Sampling—0
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation—0
Efficient-UCBV: An Almost Optimal Algorithm using Variance Estimates—0
Eluder Dimension and the Sample Complexity of Optimistic Exploration—0
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment—0
Ensemble Sampling—0
Epinet for Content Cold Start—0
Epsilon-Greedy Thompson Sampling to Bayesian Optimization—0
Estimating prediction error for complex samples—0
Estimating Quality in Multi-Objective Bandits Optimization—0
Etat de l'art sur l'application des bandits multi-bras—0
EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning—0
From Predictions to Decisions: The Importance of Joint Predictive Distributions—0
Evaluation of Explore-Exploit Policies in Multi-result Ranking Systems—0
Expected Improvement-based Contextual Bandits—0
Exploiting correlation and budget constraints in Bayesian multi-armed bandit optimization—0
A Unified Approach to Translate Classical Bandit Algorithms to the Structured Bandit Setting—0
Exploration for Multi-task Reinforcement Learning with Deep Generative Models—0
Exploration via linearly perturbed loss minimisation—0
Fast online inference for nonlinear contextual bandit based on Generative Adversarial Network—0
Online Learning with Cumulative Oversampling: Application to Budgeted Influence Maximization—0
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning—0
Feel-Good Thompson Sampling for Contextual Dueling Bandits—0
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits—0
First-Order Bayesian Regret Analysis of Thompson Sampling—0
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