The CREST Statistics group develops research and offers student training in theoretical and applied statistics.
Statistical methods play nowadays a key role in data science, machine learning and artificial intelligence. They have reached an unprecedented level of impact in recent years.
The group has got international recognition for contributions to various areas of statistical science including highdimensional statistics, machine learning, bayesian analysis, statistical computation and simulation, nonparametric estimation, statistical analysis of networks, functional data analysis, dimension reduction, statistical optimal transport, game theory, quantum statistics, time series, and extreme value theory.
Our faculty serve on editorial boards of topranked journals in statistics such as Annals of Statistics, Biometrika, Journal of the American Statistical Association, Journal of the Royal Statistical Society, and on program committees of major conferences in machine learning (NeurIPS, ICML, COLT).
The Statistics group is composed of two units, one located at ENSAE (Palaiseau, Institut Polytechnique de Paris) and the other at ENSAI (Bruz). The ENSAE unit participates in the consortia Fondation Mathématique Jacques Hadamard, Hi!Paris – Paris Artificial Intelligence for Society and Business, Center for Data Science of the University ParisSaclay. The ENSAI unit participates in the Graduate Schools DIGISPORT and CyberSchool.
The faculty teach in the Master programs “Data Science” of Institut Polytechnique de Paris, the Masters “Mathématiques, vision, apprentissage” (MVA) and “Statistics and Machine Learning” of the University ParisSaclay, and the Master “Statistics for Smart Data” of ENSAI.
Contacts
Alexandre Tsybakov (Director)
Pascale Deniau (Administrator)
Edith Verger (Administrator, CNRS)
 NovStatistics
21Fanny YANG (ETH Zurich) ” How the strength of the inductive bias affects the generalization performance of interpolators “
2:00 PM  3:15 PM  NovStatistics
28Angelika Rohde (University of Freiburg) “Sharp adaptive similarity testing with pathwise stability for ergodic diffusions”
2:00 PM  3:15 PM
2021

Adaptive robust estimation in sparse vector model
Annals of statistics, vol. 49, iss. 3, pp. 13471377, 2021.
By L. Comminges, O. Collier, M. Ndaoud, and A. B. Tsybakov
@article{CoCoNdTs2021, author={L. Comminges and O. Collier and M. Ndaoud and A.B. Tsybakov}, title={Adaptive robust estimation in sparse vector model}, journal={Annals of Statistics}, year={2021}, volume={49}, number={3}, pages={13471377}, issn={00905364}, doi={10.1214/20AOS2002}, sici={00905364(2021)49:3<1347:AREISV>2.0.CO;2H}, url={https://arxiv.org/pdf/1802.04230.pdf}, }