Inductive Venn–Abers and related regressors

Ivan Petej, Vladimir Vovk
Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 329:28-61, 2026.

Abstract

Venn–Abers predictors are probabilistic predictors that enjoy appealing properties of validity, but their major limitation is that they are applicable only to the case of binary classification, with a recent extension to bounded regression. We generalize them to the case of unbounded regression, which requires adding an element of conformal prediction. In our simulation and empirical studies we investigate the predictive efficiency of point regressors derived from Venn–Abers regressors and argue that they somewhat improve the predictive efficiency of standard regressors for larger training sets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v329-petej26a, title = {Inductive Venn–Abers and related regressors}, author = {Petej, Ivan and Vovk, Vladimir}, booktitle = {Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {28--61}, year = {2026}, editor = {Ahlberg, Ernst and Johansson, Ulf and Boström, Henrik and Carlevaro, Alberto and Hallberg Szabadváry, Johan and Carlsson, Lars}, volume = {329}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v329/main/assets/petej26a/petej26a.pdf}, url = {https://proceedings.mlr.press/v329/petej26a.html}, abstract = {Venn–Abers predictors are probabilistic predictors that enjoy appealing properties of validity, but their major limitation is that they are applicable only to the case of binary classification, with a recent extension to bounded regression. We generalize them to the case of unbounded regression, which requires adding an element of conformal prediction. In our simulation and empirical studies we investigate the predictive efficiency of point regressors derived from Venn–Abers regressors and argue that they somewhat improve the predictive efficiency of standard regressors for larger training sets.} }
Endnote
%0 Conference Paper %T Inductive Venn–Abers and related regressors %A Ivan Petej %A Vladimir Vovk %B Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2026 %E Ernst Ahlberg %E Ulf Johansson %E Henrik Boström %E Alberto Carlevaro %E Johan Hallberg Szabadváry %E Lars Carlsson %F pmlr-v329-petej26a %I PMLR %P 28--61 %U https://proceedings.mlr.press/v329/petej26a.html %V 329 %X Venn–Abers predictors are probabilistic predictors that enjoy appealing properties of validity, but their major limitation is that they are applicable only to the case of binary classification, with a recent extension to bounded regression. We generalize them to the case of unbounded regression, which requires adding an element of conformal prediction. In our simulation and empirical studies we investigate the predictive efficiency of point regressors derived from Venn–Abers regressors and argue that they somewhat improve the predictive efficiency of standard regressors for larger training sets.
APA
Petej, I. & Vovk, V.. (2026). Inductive Venn–Abers and related regressors. Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 329:28-61 Available from https://proceedings.mlr.press/v329/petej26a.html.

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