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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 221–230 of 655 papers

TitleStatusHype
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits—0
Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences ConstraintsCode0
Differentially Private Online Bayesian Estimation With Adaptive TruncationCode0
A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles—0
Thompson Sampling with Diffusion Generative Prior—0
Reinforcement Learning in Credit Scoring and Underwriting—0
Neural Bandits for Data Mining: Searching for Dangerous PolypharmacyCode0
Online Learning-based Waveform Selection for Improved Vehicle Recognition in Automotive Radar—0
Monte Carlo Tree Search Algorithms for Risk-Aware and Multi-Objective Reinforcement Learning—0
Thompson Sampling for High-Dimensional Sparse Linear Contextual BanditsCode0
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