Sub-Gaussian mean estimators L Devroye, M Lerasle, G Lugosi, RI Oliveira
237 2016 Robust machine learning by median-of-means: theory and practice G Lecué, M Lerasle
202 2020 Choice of V for V-fold cross-validation in least-squares density estimation S Arlot, M Lerasle
Journal of Machine Learning Research 17 (208), 1-50, 2016
149 2016 Robust empirical mean estimators M Lerasle, RI Oliveira
arXiv preprint arXiv:1112.3914, 2011
104 2011 Robust classification via MOM minimization G Lecué, M Lerasle, T Mathieu
Machine learning 109, 1635-1665, 2020
71 2020 Optimal change-point detection and localization N Verzelen, M Fromont, M Lerasle, P Reynaud-Bouret
The Annals of Statistics 51 (4), 1586-1610, 2023
60 2023 Learning from MOM’s principles: Le Cam’s approach G Lecué, M Lerasle
Stochastic Processes and their applications 129 (11), 4385-4410, 2019
59 2019 Kernels based tests with non-asymptotic bootstrap approaches for two-sample problems M Fromont, B Laurent, M Lerasle, P Reynaud-Bouret
Conference on Learning Theory, 23.1-23.23, 2012
57 2012 Optimal model selection in density estimation M Lerasle
Annales de l'IHP Probabilités et statistiques 48 (3), 884-908, 2012
53 2012 Lecture notes: Selected topics on robust statistical learning theory M Lerasle
arXiv preprint arXiv:1908.10761, 2019
45 * 2019 Robust statistical learning with Lipschitz and convex loss functions G Chinot, G Lecué, M Lerasle
Probability Theory and related fields 176 (3), 897-940, 2020
40 2020 Monk outlier-robust mean embedding estimation by median-of-means M Lerasle, Z Szabó, T Mathieu, G Lecué
International conference on machine learning, 3782-3793, 2019
40 2019 Optimal model selection for density estimation of stationary data under various mixing conditions M Lerasle
35 2011 Robust high dimensional learning for Lipschitz and convex losses C Geoffrey, L Guillaume, L Matthieu
Journal of Machine Learning Research 21 (233), 1-47, 2020
22 2020 On the robustness of the minimum G Chinot, M Lerasle
arXiv preprint arXiv:2003.05838, 2020
21 2020 The number of potential winners in Bradley–Terry model in random environment R Chetrite, R Diel, M Lerasle
21 2017 Family-wise separation rates for multiple testing M Fromont, M Lerasle, P Reynaud-Bouret
20 2016 Aggregated hold-out G Maillard, S Arlot, M Lerasle
Journal of Machine Learning Research 22 (20), 1-55, 2021
18 2021 Optimal kernel selection for density estimation M Lerasle, NM Magalhães, P Reynaud-Bouret
High Dimensional Probability VII: The Cargese Volume, 425-460, 2016
17 2016 Statistical learning with Lipschitz and convex loss functions G Chinot, L Guillaume, L Matthieu
arXiv preprint arXiv:1810.01090, 2018
16 2018 
Sylvain ArlotUniversité Paris-SaclayGeverifieerd e-mailadres voor universite-paris-saclay.fr
