Sharp Guarantees and Optimal Performance for Inference in Binary and Gaussian-Mixture Models
Sharp Guarantees and Optimal Performance for Inference in Binary and Gaussian-Mixture Models
We study convex empirical risk minimization for high-dimensional inference in binary linear classification under both discriminative binary linear models, as well as generative Gaussian-mixture models. Our first result sharply predicts the statistical performance of such estimators in the proportional asymptotic regime under isotropic Gaussian features. Importantly, the predictions hold for …