A Unified Comparative Study with Generalized Conformity Scores for Multi-Output Conformal Regression
Dheur, Victor Fontana, Matteo Estievenart, Yorick Desobry, Naomi Taieb, Souhaib Ben
Dheur, Victor
Fontana, Matteo
Estievenart, Yorick
Desobry, Naomi
Taieb, Souhaib Ben
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Department
Statistics and Data Science
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Type
Conference proceeding
Date
2025
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Language
English
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Abstract
Conformal prediction provides a powerful framework for constructing distribution-free prediction regions with finite-sample coverage guarantees. While extensively studied in univariate settings, its extension to multi-output problems presents additional challenges, including complex output dependencies and high computational costs, and remains relatively underexplored. In this work, we present a unified comparative study of nine conformal methods with different multivariate base models for constructing multivariate prediction regions within the same framework. This study highlights their key properties while also exploring the connections between them. Additionally, we introduce two novel classes of conformity scores for multi-output regression that generalize their univariate counterparts. These scores ensure asymptotic conditional coverage while maintaining exact finite-sample marginal coverage. One class is compatible with any generative model, offering broad applicability, while the other is computationally efficient, leveraging the properties of invertible generative models. Finally, we conduct a comprehensive empirical evaluation across 13 tabular datasets, comparing all the multi-output conformal methods explored in this work. To ensure a fair and consistent comparison, all methods are implemented within a unified code base1.
Citation
V. Dheur, M. Fontana, Y. Estievenart, N. Desobry, and S. Ben Taieb, “A Unified Comparative Study with Generalized Conformity Scores for Multi-Output Conformal Regression,” Oct. 06, 2025, PMLR. [Online]. Available: https://proceedings.mlr.press/v267/dheur25a.html
Source
Proceedings of Machine Learning Research
Conference
42nd International Conference on Machine Learning, ICML 2025
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Subjects
Source
42nd International Conference on Machine Learning, ICML 2025
Publisher
ML Research Press