AI RESEARCH
Conformal Margin Risk Minimization: An Envelope Framework for Robust Learning under Label Noise
arXiv CS.LG
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ArXi:2604.06468v1 Announce Type: new Most methods for learning with noisy labels require privileged knowledge such as noise transition matrices, clean subsets or pretrained feature extractors, resources typically unavailable when robustness is most needed. We propose Conformal Margin Risk Minimization (CMRM), a plug-and-play envelope framework that improves any classification loss under label noise by adding a single quantile-calibrated regularization term, with no privileged knowledge or