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 high-dimensional 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 top-ranked 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 Paris-Saclay. 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 Paris-Saclay, and the Master “Statistics for Smart Data” of ENSAI.

- Sep

09**Seminars, Statistics**#### Etienne BOURSIER (INRIA) “Early alignment in two-layer networks training is a two-edged sword”

2:00 PM - 3:30 PM **Sep**

16**Seminars, Statistics**#### Gil KUR (Université de Zurich)

TBA

2:00 PM - 3:30 PM

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There are no upcoming publications at this time.
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**## statistics

## Contextual Continuum Bandits: Static Versus Dynamic Regret

We study the contextual continuum bandits problem, where the learner sequentially receives a side information vector and has to choose an action in a convex set, minimizing a function associated to th ...

arXiv:2406.05714v1 [stat.ML], 2024

## statistics

## Generalized multi-view model: Adaptive density estimation under low-rank constraints

We study the problem of bivariate discrete or continuous probability density estimation under low-rank constraints.For discrete distributions, we assume that the two-dimensional array to estimate is a ...

Arxiv, Cornell University, 2024

## statistics

## Hasimoto frames and the Gibbs measure of the periodic nonlinear Schrödinger equation

The paper interprets the cubic nonlinear Schrödinger equation as a Hamiltonian system with infinite dimensional phase space. There exists a Gibbs measure which is invariant under the flow associated ...

J. Math. Phys. 65, 022705 (2024), 2024

## statistics

## Estimation of regional and at-site quantiles of extreme winds under flood index procedure

Extreme winds are becoming more common among environmental events with the most catastrophic societal consequences. A regional frequency analysis of Daily Annual Maximum Wind Speed (DAMWS) is necessar ...

Heliyon, Open Access, Volume 10, Issue 115, January 2024, Article number e23388, 2024

## statistics

## Tail inverse regression: Dimension reduction for prediction of extremes

We consider the problem of supervised dimension reduction with a particular focus on extreme values of the target Y ∈ R to be explained by a covariate vector X ∈ Rp. The general purpose is to defi ...

Bernoulli, Volume 30, Issue 1, Pages 503 - 533, 2024

## statistics

## Proxy-analysis of the genetics of cognitive decline in Parkinson’s disease through polygenic scores

Cognitive decline is common in Parkinson’s disease (PD) and its genetic risk factors are not well known to date, besides variants in the GBA and APOE genes. However, variation in complex traits is c ...

Nature Research, 2024