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03/11/2022
A geometrical viewpoint on the benign overfitting property of the minimum l_2-norm interpolant estimator
Practitioners have observed that some deep learning models generalize we...
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02/01/2021
Optimal robust mean and location estimation via convex programs with respect to any pseudo-norms
We consider the problem of robust mean and location estimation w.r.t. an...
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01/22/2021
On the robustness to adversarial corruption and to heavy-tailed data of the Stahel-Donoho median of means
We consider median of means (MOM) versions of the Stahel-Donoho outlying...
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04/04/2020
Learning with Semi-Definite Programming: new statistical bounds based on fixed point analysis and excess risk curvature
Many statistical learning problems have recently been shown to be amenab...
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06/07/2019
Robust subgaussian estimation of a mean vector in nearly linear time
We construct an algorithm, running in nearly-linear time, which is robus...
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05/10/2019
Robust high dimensional learning for Lipschitz and convex losses
We establish risk bounds for Regularized Empirical Risk Minimizers (RERM...
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12/06/2018
Median of means principle as a divide-and-conquer procedure for robustness, sub-sampling and hyper-parameters tuning
Many learning methods have poor risk estimates with large probability un...
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08/09/2018
Robust classification via MOM minimization
We present an extension of Vapnik's classical empirical risk minimizer (...
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05/17/2018
Minimax regularization
Classical approach to regularization is to design norms enhancing smooth...
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11/28/2017
Robust machine learning by median-of-means : theory and practice
We introduce new estimators for robust machine learning based on median-...
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12/08/2009
Hyper-sparse optimal aggregation
In this paper, we consider the problem of "hyper-sparse aggregation". Na...
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09/29/2005
