“Toward a better understanding of AI algorithms”. Arnak Dalalyan, professor at ENSAE Paris and director of the CREST, revolves around statistical methods for machine learning. He is going to further develop those methods in his project with Hi! PARIS entitled “Statistical Analysis of Generative Models: Sampling Guarantees and Robustness (SAGMOS)”.
‘On Future Trends and Opportunities for Monte Carlo methods’ congress in Warsaw
Nicolas Chopin will participate to the congress ‘On Future Trends and Opportunities for Monte Carlo methods’ which will take place from 07.12.2022 to 09.12.2022 in Warsaw
“Researchers are using Google to forecast economic activity”
An interview with Anna Simoni in Polytechnique Insights – Oct. 18th, 2022
An alternative to synthetic control for models with many covariates under sparsity
CREST Working Papers Series No. 2020-17
by Marianne Bléhaut, Xavier D’Haultfoeuille, Jérémy L’Hour and Alexandre Tsybakov
When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage
CREST Working Papers Series No. 2019-04
by Laurent Ferrara and Anna Simoni
Homogeneous Besov and Triebel–Lizorkin spaces associated to non-negative self-adjoint operators
CREST Working Papers Series No. 2017-92
by Athanasios G. Georgiadis, Gérard Kerkyacharian, Georges Kyriazis and Pencho Petrushev
Hardy spaces associated with non-negative self-adjoint operators
CREST Working Papers Series No. 2017-91
by Shia Dekel, Gérard Kerkyacharian, Georges Kyriazis and Pencho Petrushev
Regularity of Gaussian Processes on Dirichlet spaces
CREST Working Papers Series No. 2017-90
by Gérard Kerkyacharian, Shigeyoshi Ogawa, Pencho Petrushev and Dominique Picard
Computational Optimal Transport
CREST Working Papers Series No. 2017-86
by Gabriel Peyré and Marco Cuturi
Log-PCA versus Geodesic PCA of histograms in the Wasserstein space
CREST Working Papers Series No. 2017-85
by Elsa Cazelles, Vivien Seguy, Jérémie Bigot, Marco Cuturi and Nicolas Papadakis