Inductive Venn-Abers Predictive Distribution. / Nouretdinov, Ilia; Volkhonskiy, Denis; Lim, Pitt; Toccaceli, Paolo; Gammerman, Alexander.

7th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2018). Vol. 91 2018. p. 1-22 (Proceedings of Machine Learning Research).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Published

Documents

Links

Abstract

Venn predictors are a distribution-free probabilistic prediction framework that transforms the output of a scoring classifier into a (multi-)probabilistic prediction that has calibration guarantees, with the only requirement of an i.i.d. assumption for calibration and test data.
In this paper, we extend the framework from classification (where probabilities are predicted for a discrete number of labels) to regression (where labels form a continuum). We show how Venn Predictors can be applied on top of any regression method to obtain calibrated predictive distributions, without requiring assumptions beyond i.i.d. of calibration and test sets. This is contrasted with methods such as Bayesian Linear Regression, for which the calibration guarantee instead relies on specific probabilistic assumptions on the distribution of the data.
The adaptation of Venn Machine to regression required a theoretical analysis of the transductive and inductive forms of the predictor. We identify potential consistency problems and provide solutions for them.
Finally, to illustrate their advantages, we apply regression Venn Predictors to the medical problem of predicting the survival time after Percutaneous Coronary Intervention, a potentially risky procedure that improves blood flow to a patient’s heart. The predictive distributions obtained with this method allow a variety of interpretations that include probability of survival time exceeding a chosen threshold or the shortest survival time guaranteed with a given probability.
Original languageEnglish
Title of host publication7th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2018)
Pages1-22
Number of pages22
Volume91
StatePublished - Jun 2018
EventThe 7th Symposium on Conformal and Probabilistic Prediction with Applications: COPA 2018 - Maastricht, Netherlands
Duration: 11 Jun 201813 Jun 2018
http://www.clrc.rhul.ac.uk/copa2018/index.html

Publication series

NameProceedings of Machine Learning Research
ISSN (Electronic)1938-7228

Conference

ConferenceThe 7th Symposium on Conformal and Probabilistic Prediction with Applications
CountryNetherlands
CityMaastricht
Period11/06/1813/06/18
Internet address
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 30015382