| Sub-Gaussian mean estimators L Devroye, M Lerasle, G Lugosi, RI Oliveira Annals of Statistics 44 (6), 2695-2725, 2016 | 99 | 2016 |
| Robust empirical mean estimators M Lerasle, RI Oliveira arXiv preprint arXiv:1112.3914, 2011 | 65 | 2011 |
| Robust machine learning by median-of-means: theory and practice G Lecué, M Lerasle Annals of Statistics 48 (2), 906-931, 2020 | 58 | 2020 |
| Choice of V for V-fold cross-validation in least-squares density estimation S Arlot, M Lerasle The Journal of Machine Learning Research 17 (1), 7256-7305, 2016 | 47* | 2016 |
| Optimal model selection in density estimation M Lerasle Annales de l'IHP Probabilités et statistiques 48 (3), 884-908, 2012 | 44 | 2012 |
| Robust classification via mom minimization G Lecué, M Lerasle, T Mathieu Machine Learning 109 (8), 1635-1665, 2020 | 34 | 2020 |
| Kernels based tests with non-asymptotic bootstrap approaches for two-sample problems M Fromont, M Lerasle, P Reynaud-Bouret Conference on Learning Theory, 23.1-23.23, 2012 | 34 | 2012 |
| Optimal model selection for density estimation of stationary data under various mixing conditions M Lerasle Annals of statistics 39 (4), 1852-1877, 2011 | 30 | 2011 |
| Learning from MOM’s principles: Le Cam’s approach G Lecué, M Lerasle Stochastic Processes and their applications 129 (11), 4385-4410, 2019 | 25 | 2019 |
| Why V= 5 is enough in V-fold cross-validation S Arlot, M Lerasle arXiv preprint arXiv:1210.5830, 2012 | 16 | 2012 |
| Robust statistical learning with Lipschitz and convex loss functions G Chinot, G Lecué, M Lerasle Probability Theory and related fields, 1-44, 2019 | 12 | 2019 |
| The number of potential winners in Bradley–Terry model in random environment R Chetrite, R Diel, M Lerasle Annals of Applied Probability 27 (3), 1372-1394, 2017 | 11 | 2017 |
| Optimal kernel selection for density estimation M Lerasle, NM Magalhães, P Reynaud-Bouret High Dimensional Probability VII, 425-460, 2016 | 11 | 2016 |
| Adaptive density estimation of stationary β-mixing and τ-mixing processes M Lerasle Mathematical Methods of statistics 18 (1), 59-83, 2009 | 11 | 2009 |
| 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 | 10 | 2019 |
| Markov Approximations of chains of infinite order in the -metric S Gallo, M Lerasle, DY Takahashi arXiv preprint arXiv:1107.4353, 2011 | 10 | 2011 |
| Statistical learning with Lipschitz and convex loss functions G Chinot, L Guillaume, L Matthieu arXiv preprint arXiv:1810.01090, 2018 | 9 | 2018 |
| Sharp oracle inequalities and slope heuristic for specification probabilities estimation in discrete random fields M Lerasle, DY Takahashi Bernoulli 22 (1), 325-344, 2016 | 9 | 2016 |
| Rééchantillonnage et sélection de modèles optimale pour l’estimation de la densité de variables indépendantes ou mélangeantes M Lerasle PhD thesis, Toulouse, 2009 | 8 | 2009 |
| Family-wise separation rates for multiple testing M Fromont, M Lerasle, P Reynaud-Bouret Annals of Statistics 44 (6), 2533-2563, 2016 | 7 | 2016 |
Guillaume LecuéProfessor - ENSAE - CRESTVerified email at ensae.fr
