We study the problem of agnostically learning homogeneous halfspaces in the distribution-specific PAC model. For a broad family of structured distributions, including log-concave distributions, we show that non-convex SGD efficiently convergestoasolution withmisclassification errorO(opt)+,whereoptisthe misclassification error of the best-fitting halfspace.

The five datasets used cover a wide range of respiratory medical conditions.

However,giventhesafety-critical nature of healthcare applications, it is pivotal to also ensure openness and replicability foranyproposed foundation model solution.