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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 261–270 of 655 papers

TitleStatusHype
Adaptively Learning to Select-Rank in Online Platforms—0
Practical Batch Bayesian Sampling Algorithms for Online Adaptive Traffic Experimentation—0
From Bandits Model to Deep Deterministic Policy Gradient, Reinforcement Learning with Contextual Information—0
Online Learning with Cumulative Oversampling: Application to Budgeted Influence Maximization—0
Bayesian Optimization-Based Beam Alignment for MmWave MIMO Communication Systems—0
Feel-Good Thompson Sampling for Contextual Dueling Bandits—0
Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?—0
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits—0
First-Order Bayesian Regret Analysis of Thompson Sampling—0
A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification—0
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