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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 451–475 of 655 papers

TitleStatusHype
Non-Stationary Bandit Learning via Predictive Sampling—0
Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing—0
Non-Stationary Latent Bandits—0
No Regrets for Learning the Prior in Bandits—0
Observation-Free Attacks on Stochastic Bandits—0
On Adaptive Estimation for Dynamic Bernoulli Bandits—0
On Batch Bayesian Optimization—0
On Dynamic Pricing with Covariates—0
On Efficiency in Hierarchical Reinforcement Learning—0
On Improved Regret Bounds In Bayesian Optimization with Gaussian Noise—0
On Kernelized Multi-Armed Bandits with Constraints—0
On learning Whittle index policy for restless bandits with scalable regret—0
Online Algorithms For Parameter Mean And Variance Estimation In Dynamic Regression Models—0
Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits—0
Online Causal Inference for Advertising in Real-Time Bidding Auctions—0
Online Learning and Distributed Control for Residential Demand Response—0
Online Learning-based Waveform Selection for Improved Vehicle Recognition in Automotive Radar—0
Online Learning of Energy Consumption for Navigation of Electric Vehicles—0
Online Learning of Network Bottlenecks via Minimax Paths—0
Online Residential Demand Response via Contextual Multi-Armed Bandits—0
Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling—0
On Multi-Armed Bandit Designs for Dose-Finding Clinical Trials—0
On Online Learning in Kernelized Markov Decision Processes—0
On The Differential Privacy of Thompson Sampling With Gaussian Prior—0
On the Importance of Uncertainty in Decision-Making with Large Language Models—0
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