35 citations · 109 across the 11 of their papers we have counts for
12 papers
A MOM-based ensemble method for robustness, subsampling and hyperparameter tuning
Joon Kwon, Guillaume Lecué, Matthieu Lerasle
Hyperparameters tuning and model selection are important steps in machine learning. Unfortunately, classical hyperparameter calibration and model selection procedures are sensitive…
Robust classification via MOM minimization
Guillaume Lecué, Matthieu Lerasle, Timothée Mathieu
We present an extension of Vapnik's classical empirical risk minimizer (ERM) where the empirical risk is replaced by a median-of-means (MOM) estimator, the new estimators are calle…
MONK -- Outlier-Robust Mean Embedding Estimation by Median-of-Means
Matthieu Lerasle, Zoltan Szabo, Timothee Mathieu +1
Mean embeddings provide an extremely flexible and powerful tool in machine learning and statistics to represent probability distributions and define a semi-metric (MMD, maximum mea…
Regularization and the small-ball method II: complexity dependent error rates
Guillaume Lecué, Shahar Mendelson
For a convex class of functions , a regularization functions and given the random data , we study estimation properties of regularization procedur…
On the gap between RIP-properties and sparse recovery conditions
Sjoerd Dirksen, Guillaume Lecué, Holger Rauhut
We consider the problem of recovering sparse vectors from underdetermined linear measurements via -constrained basis pursuit. Previous analyses of this problem based on gen…
Weighted algorithms for compressed sensing and matrix completion
Stéphane Gaïffas, Guillaume Lecué
This paper is about iteratively reweighted basis-pursuit algorithms for compressed sensing and matrix completion problems. In a first part, we give a theoretical explanation of the…