The aim of the workshop "Statistics/Learning at Paris-Saclay" is to bring together mathematicians and computer scientists around some talks on recent results in statistics and machine learning. It will take place on January 19h 2018 at Bures-sur-Yvette.
Various theoretical topics in machine learning, statistics and probability theory will be presented as well as applications to social and ecological networks and brain mapping.
Organised by : Pierre Alquier et Guillaume Charpiat, with support from : GT Deep Net & GT Pasadena
Invited speakers :
- Romain Couillet (Centrale-Supéléc)
- Marco Cuturi (ENSAE ParisTech)
- Sophie Donnet (INRA)
- Anna Korba (Telecom ParisTech)
- James Ridgway (Agro ParisTech)
- Bertrand Thirion (INRIA)
The presentations will be in French, with slides (and potentially questions) in English.
On the programme:
- A random matrix approach to bigdata machine learning, Romain Couillet (Centrale-Supéléc)
- Generative Models and Optimal Transport, Marco Cuturi (ENSAE ParisTech)
- Introduction aux modèles à blocs stochastiques pour la modélisation de réseaux sociaux et écologiques, Sophie Donnet (INRA)
- Concentration of tempered posteriors and of their variational approximations, James Ridgway (Agro ParisTech)
- Toward a rigorous statistical framework for functional brain mapping, Bertrand Thirion (INRIA)
- Ranking median regression: Learning to order through local consensus, Anna Korba (Telecom ParisTech)