Universally consistent conformal predictive distributions

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This paper describes conformal predictive systems that are universally consistent in the sense of being consistent under any data-generating distribution, assuming that the observations are produced independently in the IID fashion. Being conformal, these predictive systems satisfy a natural property of small-sample validity, namely they are automatically calibrated in probability.
Original languageEnglish
Title of host publicationProceedings of Machine Learning Research
EditorsAlex Gammerman, Vladimir Vovk, Zhiyuan Luo, Evgueni Smirnov
Pages105-122
Number of pages18
Volume105
Publication statusPublished - 29 Aug 2019

Keywords

  • calibration in probability
  • conformal prediction
  • predictive distribution
  • universal consistency

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