Nonparametric predictive distributions based on conformal prediction. / Vovk, Vladimir; Shen, Jieli; Manokhin, Valery; Xie, Minge.

Proceedings of Machine Learning Research: Proceedings of COPA 2016 (Sixth Symposium on Conformal and Probabilistic Prediction and Applications). ed. / Alex Gammerman; Vladimir Vovk; Zhiyuan Luo; Harris Papadopoulos. Vol. 60 2017. p. 82-102.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Published

Standard

Nonparametric predictive distributions based on conformal prediction. / Vovk, Vladimir; Shen, Jieli; Manokhin, Valery; Xie, Minge.

Proceedings of Machine Learning Research: Proceedings of COPA 2016 (Sixth Symposium on Conformal and Probabilistic Prediction and Applications). ed. / Alex Gammerman; Vladimir Vovk; Zhiyuan Luo; Harris Papadopoulos. Vol. 60 2017. p. 82-102.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Harvard

Vovk, V, Shen, J, Manokhin, V & Xie, M 2017, Nonparametric predictive distributions based on conformal prediction. in A Gammerman, V Vovk, Z Luo & H Papadopoulos (eds), Proceedings of Machine Learning Research: Proceedings of COPA 2016 (Sixth Symposium on Conformal and Probabilistic Prediction and Applications). vol. 60, pp. 82-102.

APA

Vovk, V., Shen, J., Manokhin, V., & Xie, M. (2017). Nonparametric predictive distributions based on conformal prediction. In A. Gammerman, V. Vovk, Z. Luo, & H. Papadopoulos (Eds.), Proceedings of Machine Learning Research: Proceedings of COPA 2016 (Sixth Symposium on Conformal and Probabilistic Prediction and Applications) (Vol. 60, pp. 82-102)

Vancouver

Vovk V, Shen J, Manokhin V, Xie M. Nonparametric predictive distributions based on conformal prediction. In Gammerman A, Vovk V, Luo Z, Papadopoulos H, editors, Proceedings of Machine Learning Research: Proceedings of COPA 2016 (Sixth Symposium on Conformal and Probabilistic Prediction and Applications). Vol. 60. 2017. p. 82-102

Author

Vovk, Vladimir ; Shen, Jieli ; Manokhin, Valery ; Xie, Minge. / Nonparametric predictive distributions based on conformal prediction. Proceedings of Machine Learning Research: Proceedings of COPA 2016 (Sixth Symposium on Conformal and Probabilistic Prediction and Applications). editor / Alex Gammerman ; Vladimir Vovk ; Zhiyuan Luo ; Harris Papadopoulos. Vol. 60 2017. pp. 82-102

BibTeX

@inbook{65a68b2ba61340c4b8f03591244f80e9,
title = "Nonparametric predictive distributions based on conformal prediction",
abstract = "This paper applies conformal prediction to derive predictive distributions that are valid under a nonparametric assumption. Namely, we introduce and explore predictive distribution functions that always satisfy a natural property of validity in terms of guaranteed coverage for IID observations. The focus is on a prediction algorithm that we call the Least Squares Prediction Machine (LSPM). The LSPM generalizes the classical Dempster-Hill predictive distributions to regression problems. If the standard parametric assumptions for Least Squares linear regression hold, the LSPM is as efficient as the Dempster-Hill procedure, in a natural sense. And if those parametric assumptions fail, the LSPM is still valid, provided the observations are IID.",
keywords = "Conformal prediction., Least Squares, predictive distributions, regression",
author = "Vladimir Vovk and Jieli Shen and Valery Manokhin and Minge Xie",
year = "2017",
language = "English",
volume = "60",
pages = "82--102",
editor = "Alex Gammerman and Vladimir Vovk and Zhiyuan Luo and Harris Papadopoulos",
booktitle = "Proceedings of Machine Learning Research",

}

RIS

TY - CHAP

T1 - Nonparametric predictive distributions based on conformal prediction

AU - Vovk, Vladimir

AU - Shen, Jieli

AU - Manokhin, Valery

AU - Xie, Minge

PY - 2017

Y1 - 2017

N2 - This paper applies conformal prediction to derive predictive distributions that are valid under a nonparametric assumption. Namely, we introduce and explore predictive distribution functions that always satisfy a natural property of validity in terms of guaranteed coverage for IID observations. The focus is on a prediction algorithm that we call the Least Squares Prediction Machine (LSPM). The LSPM generalizes the classical Dempster-Hill predictive distributions to regression problems. If the standard parametric assumptions for Least Squares linear regression hold, the LSPM is as efficient as the Dempster-Hill procedure, in a natural sense. And if those parametric assumptions fail, the LSPM is still valid, provided the observations are IID.

AB - This paper applies conformal prediction to derive predictive distributions that are valid under a nonparametric assumption. Namely, we introduce and explore predictive distribution functions that always satisfy a natural property of validity in terms of guaranteed coverage for IID observations. The focus is on a prediction algorithm that we call the Least Squares Prediction Machine (LSPM). The LSPM generalizes the classical Dempster-Hill predictive distributions to regression problems. If the standard parametric assumptions for Least Squares linear regression hold, the LSPM is as efficient as the Dempster-Hill procedure, in a natural sense. And if those parametric assumptions fail, the LSPM is still valid, provided the observations are IID.

KW - Conformal prediction.

KW - Least Squares

KW - predictive distributions

KW - regression

M3 - Chapter

VL - 60

SP - 82

EP - 102

BT - Proceedings of Machine Learning Research

A2 - Gammerman, Alex

A2 - Vovk, Vladimir

A2 - Luo, Zhiyuan

A2 - Papadopoulos, Harris

ER -

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