Short Bio

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).

Curriculum Vitae

Graduate Degree "Artificial Intelligence and Advanced Visual Computing"

    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)

Awards and distinctions

Students

  1. Jaouad Mourtada (2016-)
    Ph.D. student co-supervised with Stéphane Gaïffas
  2. Nicolas Prost (2018-)
    Ph.D. student co-supervised with Julie Josse and Gaël Varoquaux
  3. Clément Bénard (2018-)
    Ph.D. student co-supervised with Gérard Biau and Sébastien Da Veiga

Publications

Preprints

  1. J Josse, N. Prost, E. Scornet, G. Varoquaux. On the consistency of supervised learning with missing values , 2019
  2. J. Mourtada, S. Gaïffas, E. Scornet. AMF: Aggregated Mondrian Forests for Online Learning , 2019
  3. C. Bénard, G. Biau, S. Da Veiga, E. Scornet. SIRUS: making random forests interpretable , 2019
  4. E. Scornet. Trees, forests, and impurity-based variable importance , 2020

Accepted/Published papers

  1. Scornet, E., Biau, G. and Vert, J.-P. (2015). Consistency of random forests, The Annals of Statistics, Vol. 43, pp. 1716-1741 (Supplementary materials ).
  2. Scornet, E. (2016). On the asymptotics of random forests, Journal of Multivariate Analysis, Vol. 146, pp. 72-83.
  3. Scornet, E. (2016). Random forests and kernel methods, IEEE Transactions on Information Theory, Vol. 62, pp. 1485-1500.
  4. Biau, G., Scornet, E. (2016). A Random Forest Guided Tour, TEST, Vol. 25, pp. 197-227. ( Discussion )
  5. Scornet, E. (2016). Promenade en forêts aléatoires, MATAPLI, Vol. 111.
  6. E. Bernard, Y. Jiao, E. Scornet, V. Stoven, T. Walter and J.-P. Vert (2017) Kernel multitask regression for toxicogenetics, Molecular Informatics, Vol. 36.
  7. J. Mourtada, S. Gaïffas, E. Scornet, (2017) Universal consistency and minimax rates for online Mondrian Forest, NIPS 2017 (Supplementary materials ).
  8. Scornet, E. (2017). Tuning parameters in random forests, ESAIM Procs, Vol. 60 pp. 144-162.
  9. R. Duroux, E. Scornet (2018) Impact of subsampling and tree depth on random forests, ESAIM: Probability and Statistics, Vol. 22, pp. 96-128
  10. G. Biau, E. Scornet, J. Welbl, (2018) Neural Random Forests , Sankhya A, pp. 1-40
  11. J. Mourtada, S. Gaïffas, E. Scornet. Minimax optimal rates for Mondrian trees and forests , to appear in the Annals of Statistics, 2019
  12. 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 in AISTAT, 2020

Academic publications

  1. PhD thesis Learning with random forests, defended on Monday, 30th November, 2015.

Teaching

You can find the slides of the Deep Learning course below
  1. Historical Neural Networks
  2. Optimization
  3. Convolutional Neural Networks
  4. Recurrent Neural Networks
  5. Generative Modelling

Talks

  1. Random Forests
  2. General overview of AI
Contact
  1. Email: prenom.nom@po-ly-tech-ni-que.edu (without hyphens).
  2. Office: 136, Turing Building, Route de Saclay, Palaiseau.
  3. Phone number: +33 1 77 57 80 80