Robust Unsupervised Learning via L-statistic Minimization

Andreas Maurer, Daniela Angela Parletta, Andrea Paudice, Massimiliano Pontil

Abstract: Designing learning algorithms that are resistant to perturbations of the underlying data distribution is a problem of wide practical and theoretical importance. We present a general approach to thi...