Minimax learning rates for estimating binary classifiers under margin conditions

ArXi:2505.10628v2 Announce Type: replace-cross We study classification problems using binary estimators where the decision boundary is described by horizon functions and where the data distribution satisfies a geometric margin condition. A key novelty of our work is the derivation of lower bounds for the worst-case learning rates over broad classes of functions, under a geometric margin condition -- a setting that is almost universally satisfied in practice, but remains theoretically challenging.