The conference is focused on the analysis of complex data and of machine learning algorithms from the point of view of mathematical statistics: minimax and Bayesian approaches, asymptotic and non-asymptotic results, adaptation for estimation or testing (learning), oracle inequalities, etc. This analysis involves advanced theories in probability, optimization and computer science.

We will put an accent on two major trends in the current research:

-matrix and more general tensor models

-optimal transport theory ​

The interactions aim at developments of new methods and theoretical guarantees in the theory of machine learning, including areas such as deep learning, robustness, topic models, learning under constraints concerning the privacy of the individuals and the fairness of the algorithms.