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The pure exploration problem with general reward functions depending on full distributions

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Publication:2102381
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DOI10.1007/s10994-022-06214-8OpenAlexW4285801517MaRDI QIDQ2102381

Yanyan Li

Publication date: 28 November 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2105.03598


zbMATH Keywords

total variation distancegeneral reward functionpure exploration


Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)




Cites Work

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  • The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality
  • A simple and effective scanning rule for a multi-channel system
  • Extremum Problems With Total Variation Distance and Their Applications
  • Multi‐Armed Bandit Allocation Indices
  • Asymptotic Minimax Character of the Sample Distribution Function and of the Classical Multinomial Estimator
  • Sequential Design of Experiments
  • On Choosing and Bounding Probability Metrics
  • On a Problem in Optimal Scanning
  • Finite-time analysis of the multiarmed bandit problem


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