Large-scale probabilistic predictors with and without guarantees of validity. / Vovk, Vladimir; Petej, Ivan; Fedorova, Valentina.

Advances in Neural Information Processing Systems 28: Proceedings of NIPS 2015. Curran Associates, 2015. p. 892-900.

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




This paper studies theoretically and empirically a method of turning machine learning algorithms into probabilistic predictors that automatically enjoys a property of validity (perfect calibration), is computationally efficient, and preserves predictive efficiency. The price to pay for perfect calibration is that these probabilistic predictors produce imprecise (in practice, almost precise for large data sets) probabilities. When these imprecise probabilities are merged into precise probabilities, the resulting predictors, while losing the theoretical property of perfect calibration, consistently outperform the existing methods in empirical studies.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 28
Subtitle of host publicationProceedings of NIPS 2015
PublisherCurran Associates
Number of pages9
Publication statusPublished - 7 Dec 2015
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 25376559