Universally consistent conformal predictive distributions. / Vovk, Vladimir.

Proceedings of Machine Learning Research. ed. / Alex Gammerman; Vladimir Vovk; Zhiyuan Luo; Evgueni Smirnov. Vol. 105 2019. p. 105-122.

Research output: Chapter in Book/Report/Conference proceedingChapter

Published

Standard

Universally consistent conformal predictive distributions. / Vovk, Vladimir.

Proceedings of Machine Learning Research. ed. / Alex Gammerman; Vladimir Vovk; Zhiyuan Luo; Evgueni Smirnov. Vol. 105 2019. p. 105-122.

Research output: Chapter in Book/Report/Conference proceedingChapter

Harvard

Vovk, V 2019, Universally consistent conformal predictive distributions. in A Gammerman, V Vovk, Z Luo & E Smirnov (eds), Proceedings of Machine Learning Research. vol. 105, pp. 105-122.

APA

Vovk, V. (2019). Universally consistent conformal predictive distributions. In A. Gammerman, V. Vovk, Z. Luo, & E. Smirnov (Eds.), Proceedings of Machine Learning Research (Vol. 105, pp. 105-122)

Vancouver

Vovk V. Universally consistent conformal predictive distributions. In Gammerman A, Vovk V, Luo Z, Smirnov E, editors, Proceedings of Machine Learning Research. Vol. 105. 2019. p. 105-122

Author

Vovk, Vladimir. / Universally consistent conformal predictive distributions. Proceedings of Machine Learning Research. editor / Alex Gammerman ; Vladimir Vovk ; Zhiyuan Luo ; Evgueni Smirnov. Vol. 105 2019. pp. 105-122

BibTeX

@inbook{627cf4416ea34f5aaa21bb9bd9ed7780,
title = "Universally consistent conformal predictive distributions",
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.",
keywords = "calibration in probability, conformal prediction, predictive distribution, universal consistency",
author = "Vladimir Vovk",
year = "2019",
month = "8",
day = "29",
language = "English",
volume = "105",
pages = "105--122",
editor = "Alex Gammerman and Vladimir Vovk and Zhiyuan Luo and Evgueni Smirnov",
booktitle = "Proceedings of Machine Learning Research",

}

RIS

TY - CHAP

T1 - Universally consistent conformal predictive distributions

AU - Vovk, Vladimir

PY - 2019/8/29

Y1 - 2019/8/29

N2 - 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.

AB - 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.

KW - calibration in probability

KW - conformal prediction

KW - predictive distribution

KW - universal consistency

M3 - Chapter

VL - 105

SP - 105

EP - 122

BT - Proceedings of Machine Learning Research

A2 - Gammerman, Alex

A2 - Vovk, Vladimir

A2 - Luo, Zhiyuan

A2 - Smirnov, Evgueni

ER -