Guillaume Lecué graduated from Ecole Normale Supérieure de Cachan, France, and received the M.Sc. degree in applied mathematics from Université Paris XI – Orsay, France, in 2005. He received the Ph.D. degree in statistics at Université Paris VI – Jussieu, France, in 2007. In 2008, he went to the Mathematical Science Institue in Canberra, Australia and later at the Technion, Haïfa, Israel as a Post-Doc. He completed his habilitation degree in 2008 at the Laboratoire d'analyse et mathématiques appliquées, Université Paris-Est Marne-la-vallée, France.
He is currently Full professor at ESSEC. His research interests are in the areas of learning theory, empirical process theory, high-dimensional phenomenons and deep learning. He taught at Ecole Polytechnqiue from 2012 to 2015 and at ENSAE (the national school of administration and statistics), France from 2015 to 2022.
Dr. Lecué received the "Mark Fulk award" for the best student paper at the 2006 Conference on Learning Theory, COLT06, Pittsburgh, PA and the "Prix de la chancellerie des Universités de Paris" for the best Ph.D. thesis in mathematics and its applications defended in Paris in 2007.
- 2012: Doctorat, Autre, Mathématiques (Université Paris Est Créteil France)
- 2007: Doctorat, Mathématiques (Université Pierre et Marie Curie (UPMC) France)
- 2004: Ecole d’ingénieur, Mathématiques (École Normale Supérieure de Rennes France)
- 2022 – Now : Professor (ESSEC Business School France)
Presentations at an Academic or Professional conference
- LECUE, G. et NEIRAC, L. (2023). Learning with a linear loss function. Excess risk and estimation bounds for ERM, minmax MOM and their regularized versions. Applications to robustness in sparse PCA. Dans: 2024 Meeting in Mathematical Statistics Conference, CIRM. Marseille.
- LECUE, G. et SHANG, Z. (2023). A Geometrical Viewpoint on the Benign Overfitting Property of the Minimum l2-norm Interpolant Estimator. Dans: 2023 Mini-Workshop: Interpolation and Over-parameterization in Statistics and Machine Learning. Oberwolfach.
- DEPERSIN, J. et LECUE, G. (2023). On the robustness to adversarial corruption and to heavy-tailed data of the Stahel–Donoho median of means. Information and Inference: A Journal of the IMA, 12(2), pp. 814-850.
- DEPERSIN, J. et LECUE, G. (2022). Optimal robust mean and location estimation via convex programs with respect to any pseudo-norms. Probability Theory and Related Fields, 183(3-4), pp. 997-1025.
- DEPERSIN, J. et LECUE, G. (2022). Robust sub-Gaussian estimation of a mean vector in nearly linear time. Annals of Statistics, 50(1), pp. 511-536.
- CHINOT, G., LECUE, G. et LERASLE, M. (2021). Robust high dimensional learning for Lipschitz and convex losses. Journal of Machine Learning Research, (233), pp. 1-47.
- CHRÉTIEN, S., CUCURINGU, M., LECUE, G. et NEIRAC, L. (2021). Learning with semi-definite programming: statistical bounds based on fixed point analysis and excess risk curvature. Journal of Machine Learning Research, 22(230), pp. 1-64.
- KWON, J., LECUE, G. et LERASLE, M. (2021). A MOM-based ensemble method for robustness, subsampling and hyperparameter tuning. The Electronic Journal of Statistics, 15(1), pp. 1202-1207.
- LECUE, G. et LERASLE, M. (2020). Robust machine learning by median-of-means: Theory and practice. Annals of Statistics, 48(2).
- LECUE, G. et LERASLE, M. (2019). Learning from MOM’s principles: Le Cam’s approach. Stochastic Processes and their Applications, 129(11), pp. 4385-4410.
- ALQUIER, P., COTTET, V. et LECUE, G. (2019). Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functions. Annals of Statistics, 47(4), pp. 2117-2144.
- BELLEC, P.C., LECUE, G. et TSYBAKOV, A.B. (2018). Slope Meets Lasso: Improved Oracle Bounds and Optimality. Annals of Statistics, 46(6B), pp. 3603-3642.
- LECUE, G. et MENDELSON, S. (2018). Regularization and the small-ball method I: Sparse recovery. Annals of Statistics, 46(2), pp. 611-641.
Senior or Associate Editor
- 2022 – Now: Rédacteur adjoint – ALEA