Statistical learning with extremes,
Master MVA, université Paris-Saclay
Joint course with Stephan Clemencon, Antoine Doize, Emilie Chautru.
Location/Schedule in 2026: at Telecom Paris (Palaiseau), on Thursdays, 9:30--12:30, rooms: following this schedule
Registration (mandatory) for 2026
Course material
Additional material (MLExtreme package)
The MLExtreme package contains a variety of basic tools and learning algorithms implementing Machine Learning algorithms for Extreme Value Analysis that have been developed in recent years. Install the package with pip:pip install MLExtreme
Or visit the github https://github.com/hi-paris/MLExtreme/
Recommended reading
The second part of this course focuses on statistical learning theory for extreme value analysis. A convenient reference is the survey paper co-written by Stephan Clemencon and myself here.For a broader overview of rececnt development in statstical learning for EVA, see also the review paper here.
It is highly recommended to familiarize yourself first with basic statistical learning theory. To do this, take Nicolas Vayatis' course 'Introduction to Statistical Learning'.
As for general references, good starting points are
- The book Foundations of Machine Learning (Mohri et al.), Chapters 2,3;
- G. Lugosi's
lecture notes on the topic of binary classification. The
first four sections are particularly relevant to our context.
N.B. These lecture notes are part of the book `Principles of Nonparametric Learning' .