Machine learning, a field at the intersection of computer science and applied mathematics, aims to design automated decision-making models for the purposes of prediction or explanation, using data or experience, and that improve over time.
In the age of big data, information is ubiquitous and no longer on a human scale. This necessitates the use and development of automated learning methods.
Our studies on machine learning cover the full spectrum of this research field, from its theoretical and algorithmic foundations to the most advanced applications.
Conducted in our LAMSADE and CEREMADE laboratories, this research is on the following topics:
Alexandre Allauzen, Jamal Atif, Tristan Cazenave, Yann Chevaleyre, Jerome Lang, Rida Laraki, Florian Yger, Benjamin Negrevergne, Clément Royer, Fabrice Rossi, Christian Robert, Julien Stoehlr, Marc Hoffmann, Robin Ryder, Vincent Rivoirard, Laurent Cohen, Irene Waldspurger, Emmanuel Bacry