Linear-Core Surrogates: Smooth Loss Functions with Linear Rates for Classification and Structured Prediction

arXiv:2604. 27742v2 Announce Type: replace Abstract: A fundamental dichotomy in the theory of classification sets smoothness against statistical efficiency: smooth surrogate losses such as the logistic loss enable fast $O(1/T)$ optimization but yield slow square-root $H$-consistency bounds, while piecewise-linear losses like the Hinge loss achieve optimal linear $H$-consistency rates but are non-differentiable.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.