Practitioners have observed that some deep learning models generalize we...
We consider the problem of robust mean and location estimation w.r.t. an...
We consider median of means (MOM) versions of the Stahel-Donoho outlying...
Many statistical learning problems have recently been shown to be amenab...
We construct an algorithm, running in nearly-linear time, which is robus...
We establish risk bounds for Regularized Empirical Risk Minimizers (RERM...
Many learning methods have poor risk estimates with large probability un...
We present an extension of Vapnik's classical empirical risk minimizer (...
Classical approach to regularization is to design norms enhancing smooth...
We introduce new estimators for robust machine learning based on
median-...
In this paper, we consider the problem of "hyper-sparse aggregation". Na...
We consider the problem of adaptation to the margin and to complexity in...