Conformal risk control for non-monotonic losses.

Authors

Anastasios N Angelopoulos

Published in

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences. Volume 384. Issue 2327. Aug 27, 2026.

Abstract

Conformal risk control is an extension of conformal prediction for controlling risk functions beyond miscoverage. The original algorithm controls the expected value of a loss that is monotonic in a one-dimensional parameter. Here, we present risk control guarantees for generic algorithms applied to possibly non-monotonic losses with multi-dimensional parameters. The guarantees depend on the stability of the algorithm-unstable algorithms have looser guarantees. We give applications of this technique to selective image classification, false discovery rate and intersection-over-union control of tumour segmentations and multi-group debiasing of recidivism predictions across overlapping race and sex groups using empirical risk minimization. This article is part of the theme issue 'Advancing uncertainty quantification in AI systems'.

PMID:
42656155
Bibliographic data and abstract were imported from PubMed on 27 Aug 2026.

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