Robust learning and complexity dependent bounds for regularized problems
Geoffrey Chinot
Abstract:We study Regularized Empirical Risk Minimizers (RERM) and minmax Median-Of-Means (MOM) estimators where the regularization function φ(•) is an even convex function. We obtain bounds on the L 2 -estimation error and the excess risk that depend on φ(f * ), where f * is the minimizer of the risk over a class F . The estimators are based on loss functions that are both Lipschitz and convex. Results for the RERM are derived under weak assumptions on the outputs and a sub-Gaussian assumption on the class {(f − f * )… Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.