Towards Group-Conditional Coverage Guarantees via Group-Invariant Representations

Paul Melki, Lionel Bombrun, Jean-Pierre Da Costa
Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 329:1046-1048, 2026.

Abstract

Split-conformal prediction is a solid framework that transforms any point predictor into a conformal set predictor with marginal coverage guarantees. In cases where observations are divided into different groups, these “average” guarantees may not be enough. Group-conditional coverage cannot be maintained, by default, using marginal calibration since different groups have different distributions of nonconformity scores. In this short paper, we propose a methodology for obtaining equalized group-conditional coverage via marginal calibration by learning group-invariant representations that disentangle the relationship between group-specific information and the nonconformity scores.

Cite this Paper

BibTeX
@InProceedings{pmlr-v329-melki26a, title = {Towards Group-Conditional Coverage Guarantees via Group-Invariant Representations}, author = {Melki, Paul and Bombrun, Lionel and Da Costa, Jean-Pierre}, booktitle = {Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {1046--1048}, 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/melki26a/melki26a.pdf}, url = {https://proceedings.mlr.press/v329/melki26a.html}, abstract = {Split-conformal prediction is a solid framework that transforms any point predictor into a conformal set predictor with marginal coverage guarantees. In cases where observations are divided into different groups, these “average” guarantees may not be enough. Group-conditional coverage cannot be maintained, by default, using marginal calibration since different groups have different distributions of nonconformity scores. In this short paper, we propose a methodology for obtaining equalized group-conditional coverage via marginal calibration by learning group-invariant representations that disentangle the relationship between group-specific information and the nonconformity scores.} }
Endnote
%0 Conference Paper %T Towards Group-Conditional Coverage Guarantees via Group-Invariant Representations %A Paul Melki %A Lionel Bombrun %A Jean-Pierre Da Costa %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-melki26a %I PMLR %P 1046--1048 %U https://proceedings.mlr.press/v329/melki26a.html %V 329 %X Split-conformal prediction is a solid framework that transforms any point predictor into a conformal set predictor with marginal coverage guarantees. In cases where observations are divided into different groups, these “average” guarantees may not be enough. Group-conditional coverage cannot be maintained, by default, using marginal calibration since different groups have different distributions of nonconformity scores. In this short paper, we propose a methodology for obtaining equalized group-conditional coverage via marginal calibration by learning group-invariant representations that disentangle the relationship between group-specific information and the nonconformity scores.
APA
Melki, P., Bombrun, L. & Da Costa, J.. (2026). Towards Group-Conditional Coverage Guarantees via Group-Invariant Representations. Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 329:1046-1048 Available from https://proceedings.mlr.press/v329/melki26a.html.

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