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Conformal Predictive Distributions with Kernels

  • Vladimir VovkEmail author
  • Ilia Nouretdinov
  • Valery Manokhin
  • Alex Gammerman
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11100)

Abstract

This paper reviews the checkered history of predictive distributions in statistics and discusses two developments, one from recent literature and the other new. The first development is bringing predictive distributions into machine learning, whose early development was so deeply influenced by two remarkable groups at the Institute of Automation and Remote Control. As result, they become more robust and their validity ceases to depend on Bayesian or narrow parametric assumptions. The second development is combining predictive distributions with kernel methods, which were originated by one of those groups, including Emmanuel Braverman. As result, they become more flexible and, therefore, their predictive efficiency improves significantly for realistic non-linear data sets.

Keywords

Conformal prediction Fiducial inference Predictive distributions 

Notes

Acknowledgements

This work has been supported by the EU Horizon 2020 Research and Innovation programme (in the framework of the ExCAPE project under grant agreement 671555) and Astra Zeneca (in the framework of the project “Machine Learning for Chemical Synthesis”).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Vladimir Vovk
    • 1
    Email author
  • Ilia Nouretdinov
    • 1
  • Valery Manokhin
    • 1
  • Alex Gammerman
    • 1
  1. 1.Royal HollowayUniversity of LondonEgham, SurreyUK

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