Robust statistical learning with Lipschitz and convex loss functions

DOI10.1007/s00440-019-00931-3zbMath1436.62178OpenAlexW2954816895WikidataQ127581162 ScholiaQ127581162MaRDI QIDQ2174664

Matthieu Lerasle, Guillaume Lecué, Geoffrey Chinot

Publication date: 21 April 2020

Published in: Probability Theory and Related Fields (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00440-019-00931-3

zbMATH Keywords

robustnessstatistical learning

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric robustness (62G35) Learning and adaptive systems in artificial intelligence (68T05) Probability theory on linear topological spaces (60B11)

Related Items (13)

Aggregated hold out for sparse linear regression with a robust loss function ⋮ Concentration study of M-estimators using the influence function ⋮ High-dimensional robust regression with \(L_q\)-loss functions ⋮ Statistical performance of quantile tensor regression with convex regularization ⋮ Robust classification via MOM minimization ⋮ ERM and RERM are optimal estimators for regression problems when malicious outliers corrupt the labels ⋮ Unnamed Item ⋮ Iteratively reweighted \(\ell_1\)-penalized robust regression ⋮ A statistical learning assessment of Huber regression ⋮ Finite sample properties of parametric MMD estimation: robustness to misspecification and dependence ⋮ Total variation regularized Fréchet regression for metric-space valued data ⋮ Suboptimality of constrained least squares and improvements via non-linear predictors ⋮ Distribution-free robust linear regression

Uses Software

• Scikit

Cites Work

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