Optimization Chair of Grenoble IA Institute (MIAI)
Scope and goals
In this chair, we intend to bring together state-of-the-art optimization and game-theoretic methodologies to advance the mathematical foundations of AI. More specifically, our objective is to leverage the specific structure of optimization problems that arise in machine learning in order to (i) provide tight theoretical guarantees for robust/adversarial learning and (ii) design new distributed/online optimization algorithms for machine learning problems.
Members
- Jérôme Malick, CNRS, LJK
- Franck Iutzeler, UGA, LJK
- Roland Hildebrand, CNRS, LJK
- Panayotis Mertikopoulos CNRS, LIG
- Yurii Nesterov, UCL, Louvain (Belgium)
- + talented students: Yu-Guan Hiesh, Selim Chraibi, Yassine Laguel, Matthias Chastan, Yassine Laguel, Waiss Azizian