Since september 2016, I am an assistant professor at the Center for Applied Mathematics (CMAP) in Ecole Polytechnique near Paris. My research interests focus on theoretical statistics and Machine Learning with a particular emphasis on nonparametric estimates. I did my PhD thesis on a particular algorithm of Machine Learning called random forests, under the supervision of Gérard Biau (LSTA - Paris 6) and Jean-Philipe Vert (Institut Curie).
A new graduate degree on Artificial Intelligence opened in September 2018 at Ecole Polytechnique.
The official training website is here
and additional information on the scientific
content can be found here .
A short summary can also be found here (presentation of March 2019)
If you are interested, applications are here.
- Winner of the Jacques Neveu 2016 Prize for a thesis in the field of probability or statistic.
- J Josse, N. Prost, E. Scornet, G. Varoquaux. On the consistency of supervised learning with missing values , 2019
- J. Mourtada, S. Gaïffas, E. Scornet. AMF: Aggregated Mondrian Forests for Online Learning , 2019
- C. Bénard, G. Biau, S. Da Veiga, E. Scornet. SIRUS: making random forests interpretable , 2019
- E. Scornet. Trees, forests, and impurity-based variable importance , 2020
- Scornet, E., Biau, G. and Vert, J.-P. (2015). Consistency of random forests, The Annals of Statistics, Vol. 43, pp. 1716-1741 (Supplementary materials ).
- Scornet, E. (2016). On the asymptotics of random forests, Journal of Multivariate Analysis, Vol. 146, pp. 72-83.
- Scornet, E. (2016). Random forests and kernel methods, IEEE Transactions on Information Theory, Vol. 62, pp. 1485-1500.
- Biau, G., Scornet, E. (2016). A Random Forest Guided Tour, TEST, Vol. 25, pp. 197-227. ( Discussion )
- Scornet, E. (2016). Promenade en forêts aléatoires, MATAPLI, Vol. 111.
- E. Bernard, Y. Jiao, E. Scornet, V. Stoven, T. Walter and J.-P. Vert (2017) Kernel multitask regression for toxicogenetics, Molecular Informatics, Vol. 36.
- J. Mourtada, S. Gaïffas, E. Scornet, (2017) Universal consistency and minimax rates for online Mondrian Forest, NIPS 2017 (Supplementary materials ).
- Scornet, E. (2017). Tuning parameters in random forests, ESAIM Procs, Vol. 60 pp. 144-162.
- R. Duroux, E. Scornet (2018) Impact of subsampling and tree depth on random forests, ESAIM: Probability and Statistics, Vol. 22, pp. 96-128
- G. Biau, E. Scornet, J. Welbl, (2018) Neural Random Forests , Sankhya A, pp. 1-40
- J. Mourtada, S. Gaïffas, E. Scornet. Minimax optimal rates for Mondrian trees and forests , to appear in the Annals of Statistics, 2019
- M. Le Morvan, N. Prost, J. Josse, E. Scornet. & G. Varoquaux. Linear predictor on linearly-generated data with missing values: non consistency and solutions, accepted for publication in AISTAT, 2020
- PhD thesis Learning with random forests, defended on Monday, 30th November, 2015.
- Email: email@example.com (without hyphens).
- Office: 136, Turing Building, Route de Saclay, Palaiseau.
- Phone number: +33 1 77 57 80 80