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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 501–525 of 655 papers

TitleStatusHype
Randomised Bayesian Least-Squares Policy Iteration—0
Sampling Acquisition Functions for Batch Bayesian Optimization—0
On Multi-Armed Bandit Designs for Dose-Finding Clinical Trials—0
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy CriticsCode0
Meta Dynamic Pricing: Transfer Learning Across Experiments—0
Constrained Thompson Sampling for Wireless Link Optimization—0
Fully Distributed Bayesian Optimization with Stochastic Policies—0
Multi-Armed Bandit Strategies for Non-Stationary Reward Distributions and Delayed Feedback Processes—0
Scalable Thompson Sampling via Optimal Transport—0
Thompson Sampling with Information Relaxation PenaltiesCode0
KLUCB Approach to Copeland Bandits—0
First-Order Bayesian Regret Analysis of Thompson Sampling—0
Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model—0
Thompson Sampling for a Fatigue-aware Online Recommendation SystemCode0
Parallel Contextual Bandits in Wireless Handover Optimization—0
Information-Directed Exploration for Deep Reinforcement LearningCode0
MergeDTS: A Method for Effective Large-Scale Online Ranker EvaluationCode0
Thompson Sampling for Noncompliant Bandits—0
Bandit Learning with Implicit FeedbackCode0
Optimal Learning for Dynamic Coding in Deadline-Constrained Multi-Channel Networks—0
Adapting multi-armed bandits policies to contextual bandits scenariosCode0
Thompson Sampling for Pursuit-Evasion Problems—0
Practical Bayesian Learning of Neural Networks via Adaptive Optimisation MethodsCode0
A Unified Approach to Translate Classical Bandit Algorithms to the Structured Bandit Setting—0
Combining Bayesian Optimization and Lipschitz Optimization—0
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