Aggregation in conformal e-classification
Machine Learning
2026-05-11 v1
Abstract
Aggregating conformal predictors is a standard way of balancing their predictive and computational efficiency while retaining their validity, at least approximately. An important advantage of conformal e-predictors is that they are easier to aggregate without sacrificing their validity. This paper studies experimentally cross-conformal e-prediction, which is an existing method of aggregating conformal e-predictors, and its modifications that are conceptually simpler and more flexible.
Cite
@article{arxiv.2605.07963,
title = {Aggregation in conformal e-classification},
author = {Vladimir Vovk},
journal= {arXiv preprint arXiv:2605.07963},
year = {2026}
}
Comments
23 pages, 10 figures