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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 401–425 of 655 papers

TitleStatusHype
Causal Bandits without prior knowledge using separating sets—0
Thompson Sampling for Unsupervised Sequential Selection—0
A Change-Detection Based Thompson Sampling Framework for Non-Stationary Bandits—0
Efficient Online Learning for Cognitive Radar-Cellular Coexistence via Contextual Thompson Sampling—0
Contextual Bandits for Advertising Budget Allocation—0
Near Optimal Adversarial Attacks on Stochastic Bandits and Defenses with Smoothed Responses—0
Reinforcement Learning with Trajectory Feedback—0
Lenient Regret for Multi-Armed Bandits—0
IntelligentPooling: Practical Thompson Sampling for mHealth—0
Greedy Bandits with Sampled Context—0
Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems—0
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural ProcessesCode1
Variable Selection via Thompson Sampling—0
Policy Gradient Optimization of Thompson Sampling Policies—0
Asynchronous Multi Agent Active Search—0
Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect—0
Constrained Thompson Sampling for Real-Time Electricity Pricing with Grid Reliability Constraints—0
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring—0
Latent Bandits Revisited—0
Hypermodels for Exploration—0
TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation—0
On Frequentist Regret of Linear Thompson Sampling—0
Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits—0
Scalable Thompson Sampling using Sparse Gaussian Process Models—0
Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization—0
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