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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

Showing 211220 of 655 papers

TitleStatusHype
Reinforcement Learning in Credit Scoring and Underwriting0
Neural Bandits for Data Mining: Searching for Dangerous PolypharmacyCode0
Online Learning-based Waveform Selection for Improved Vehicle Recognition in Automotive Radar0
Monte Carlo Tree Search Algorithms for Risk-Aware and Multi-Objective Reinforcement Learning0
Thompson Sampling for High-Dimensional Sparse Linear Contextual BanditsCode0
Atlas: Automate Online Service Configuration in Network SlicingCode0
Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning ApproachCode0
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks0
Sample-Then-Optimize Batch Neural Thompson SamplingCode1
Deep Active Ensemble Sampling For Image Classification0
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