ReCIRC: 校正的保形风险控制
ReCIRC: Rectified Conformal Risk Control
ReCIRC算法解决了保形风险控制中单一阈值无法适应不同输入条件风险的问题，在多个任务上表现优异。
ReCIRC算法通过反转输入的局部风险曲线，将单一阈值重新参数化为风险预算a，实现条件风险控制。该算法在三个合成和五个真实数据集上测试，涵盖分割、多标签和多类分类及回归任务。ReCIRC在每个设置中都获得了最低的平均最差组风险和平均正组超额风险，同时保持边际风险接近目标值。
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 target conditional risk, and then applies CRC unchanged to the resulting family. ReCIRC retains CRC's finite-sample marginal guarantee regardless of the accuracy of the estimated curves, while accurate curves yield approximate conditional risk control and, under additional conditions, asymptotically exact conditional risk control; they also support a risk-calibration diagnostic. Across three synthetic and five real-data settings spanning segmentation, multilabel and multiclass classification, and regression, ReCIRC attained the lowest average worst-group risk and mean positive group excess in every setting, while maintaining marginal risk close to the target, whereas changes in prediction size were application-dependent.
