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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 391400 of 655 papers

TitleStatusHype
Asynchronous ε-Greedy Bayesian OptimisationCode0
Online Learning and Distributed Control for Residential Demand Response0
Effects of Model Misspecification on Bayesian Bandits: Case Studies in UX Optimization0
Neural Thompson SamplingCode1
Stage-wise Conservative Linear Bandits0
Neural Model-based Optimization with Right-Censored Observations0
Position-Based Multiple-Play Bandits with Thompson Sampling0
Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control0
Partially Observable Online Change Detection via Smooth-Sparse Decomposition0
Bandits Under The Influence (Extended Version)0
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