Summary
A new arXiv paper (2605.06646) by Ivan Petej and Vladimir Vovk extends Venn-Abers predictors to unbounded regression. Venn-Abers predictors are probabilistic predictors valued for their validity guarantees, but were originally limited to binary classification and recently extended to bounded regression. The authors generalize the framework to unbounded regression by incorporating elements of conformal prediction. Through simulation and empirical studies, they examine the predictive efficiency of point regressors derived from Venn-Abers regressors and argue that these derived regressors improve, to some extent, on the predictive efficiency of standard regressors when trained on larger training sets. The work is relevant to machine learning researchers interested in calibrated probabilistic prediction, conformal prediction, and regression with validity guarantees.
Paper Overview
Research Area: Machine Learning
Authors: Ivan Petej, Vladimir Vovk
Published: 2026-05-07
arXiv: 2605.06646
Abstract
Venn-Abers predictors are probabilistic predictors with attractive properties such as validity, but their main limitation has been that they apply only to binary classification; a recent extension covers bounded regression. This paper generalizes the framework to unbounded regression, which requires adding elements of conformal prediction.
In simulation and empirical studies, the authors investigate the predictive efficiency of point regressors derived from Venn-Abers regressors, and argue that these derived regressors improve, to some extent, on the predictive efficiency of standard regressors for larger training sets.
---
*Automatically collected on 2026-05-10.*
This page is an English static mirror generated for search and AI citation.
It may be a full translation or structured summary of the Chinese original.
Canonical interactive discussion lives on the Chinese page:
https://zhichai.net/topic/177619698