Conformal calibrators

2019/02/18 by Vladimir Vovk, Vovk, Vladimir, Ivan Petej +5 · 4 citations
Decision Sciences · Engineering · #68T05 #Control Systems and Identification #FOS: Computer and information sciences #Fault Detection and Control Systems #I.2.6 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Probabilistic and Robust Engineering Design

paper · pdf · doi:10.48550/arxiv.1902.06579

openalex publication_date 2019/02/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Most existing examples of full conformal predictive systems, split-conformal predictive systems, and cross-conformal predictive systems impose severe restrictions on the adaptation of predictive distributions to the test object at hand. In this paper we develop split-conformal and cross-conformal predictive systems that are fully adaptive. Our method consists in calibrating existing predictive systems; the input predictive system is not supposed to satisfy any properties of validity, whereas the output predictive system is guaranteed to be calibrated in probability. It is interesting that the method may also work without the IID assumption, standard in conformal prediction.

Citations

• Remarks on a Multivariate Transformation

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