| Sub-Gaussian mean estimators L Devroye, M Lerasle, G Lugosi, RI Oliveira | 233 | 2016 |
| Robust machine learning by median-of-means: theory and practice G Lecué, M Lerasle | 198 | 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 | 145 | 2016 |
| Robust empirical mean estimators M Lerasle, RI Oliveira arXiv preprint arXiv:1112.3914, 2011 | 101 | 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 | 58 | 2023 |
| Learning from MOM’s principles: Le Cam’s approach G Lecué, M Lerasle Stochastic Processes and their applications 129 (11), 4385-4410, 2019 | 58 | 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 | 55 | 2012 |
| Optimal model selection in density estimation M Lerasle Annales de l'IHP Probabilités et statistiques 48 (3), 884-908, 2012 | 52 | 2012 |
| Lecture notes: Selected topics on robust statistical learning theory M Lerasle arXiv preprint arXiv:1908.10761, 2019 | 41* | 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 | 21 | 2020 |
| The number of potential winners in Bradley–Terry model in random environment R Chetrite, R Diel, M Lerasle | 21 | 2017 |
| On the robustness of the minimum interpolator G Chinot, M Lerasle arXiv preprint arXiv:2003.05838, 2020 | 20 | 2020 |
| Family-wise separation rates for multiple testing M Fromont, M Lerasle, P Reynaud-Bouret | 19 | 2016 |
| Aggregated hold-out G Maillard, S Arlot, M Lerasle Journal of Machine Learning Research 22 (20), 1-55, 2021 | 17 | 2021 |
| Statistical learning with Lipschitz and convex loss functions G Chinot, L Guillaume, L Matthieu arXiv preprint arXiv:1810.01090, 2018 | 16 | 2018 |
| Optimal kernel selection for density estimation M Lerasle, NM Magalhães, P Reynaud-Bouret High Dimensional Probability VII: The Cargese Volume, 425-460, 2016 | 16 | 2016 |
Sylvain ArlotUniversité Paris-SaclayE-mailová adresa ověřena na: universite-paris-saclay.fr