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

In: Machine Learning, Vol. 108, 15.03.2019, p. 445-474.

Research output: Contribution to journalArticlepeer-review



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 nonparametric 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.
Original languageEnglish
Pages (from-to)445-474
Number of pages30
JournalMachine Learning
Early online date17 Aug 2018
Publication statusPublished - 15 Mar 2019


This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

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