Macro Mondrian Conformal Risk Control
Abstract
Conformal Risk Control (CRC) extends conformal prediction beyond the coverage setting to the control of general monotone loss functions, in a distribution-free and modelagnostic fashion. This makes it well-suited to problems with high-dimensional targets, such as the semantic segmentation task addressed in this work. However, CRC guarantees only marginal control, meaning that the risk is controlled on average over the data distribution. Under imbalanced distributions, the risk on inputs that contain rare objects may therefore remain uncontrolled, even when the marginal guarantee holds. We propose a new method that controls the risk on average over user-defined groups of the domain, drawing on Mondrian conformal prediction and macro-coverage algorithms. The method relies on a taxonomy, that is, a function that assigns a group to each data point, and reweights each point's risk by a coefficient that depends on its group probability, so that rare points contribute more than common ones. We illustrate the approach on two semantic segmentation datasets.
Related
- Tilted Mondrian: Triage and Mitigation of Fine-grained Disparities in Conformal Prediction
- Conformal Risk-Averse Decision Making with Optimized Certainty Equivalent Risk Control
- ReCIRC: Rectified Conformal Risk Control
- Conformal risk control for model-form uncertainty in parametric non-intrusive reduced-order models
- Recertifying decisions via e-closure: uniform improvements of conformal risk control and randomized e-BH
BibTeX
@misc{combes2026macro,
title = {{Macro Mondrian Conformal Risk Control}},
author = {Julien Combes and Jean-Francois Coeurjolly and Alexandre Dervillé},
howpublished = {HAL},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.35035},
eprint = {2610.35035},
archivePrefix = {arXiv},
url = {https://hal.science/hal-05825082v1},
}
RIS
TY - UNPB
TI - Macro Mondrian Conformal Risk Control
AU - Julien Combes
AU - Jean-Francois Coeurjolly
AU - Alexandre Dervillé
PY - 2026
DA - 2026/10/20
PB - HAL
DO - 10.48550/arxiv.2610.35035
UR - https://hal.science/hal-05825082v1
AB - Conformal Risk Control (CRC) extends conformal prediction beyond the coverage setting to the control of general monotone loss functions, in a distribution-free and modelagnostic fashion. This makes it well-suited to problems with high-dimensional targets, such as the semantic segmentation task addressed in this work. However, CRC guarantees only marginal control, meaning that the risk is controlled on average over the data distribution. Under imbalanced distributions, the risk on inputs that contain rare objects may therefore remain uncontrolled, even when the marginal guarantee holds. We propose a new method that controls the risk on average over user-defined groups of the domain, drawing on Mondrian conformal prediction and macro-coverage algorithms. The method relies on a taxonomy, that is, a function that assigns a group to each data point, and reweights each point's risk by a coefficient that depends on its group probability, so that rare points contribute more than common ones. We illustrate the approach on two semantic segmentation datasets.
ER -