ReCIRC: Rectified Conformal Risk Control
Many applications of black-box predictive models require controlling task-relevant error rates, such as missed lesion pixels in segmentation or missed labels in multilabel classification. Conformal risk control (CRC; Angelopoulos et al., arXiv:2208.02814) gives distribution-free guarantees for such losses, but it calibrates a single threshold shared by all inputs. Because conditional risk varies with the input, this marginal guarantee often overprotects easy cases and underprotects hard ones. We propose ReCIRC (Rectified Conformal Risk Control), which inverts each input's estimated local risk curve to reparameterize the calibrated threshold as a risk budget $a$ representing a common targe...
| Authors | Bruno Marcondes e Resende, Helton Graziadei, Thiago Rodrigo Ramos et al. |
|---|---|
| Primary category | stat.ML |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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