Learning and Optimization

Research Themes

The A&O team – a joint Paris-Saclay, CNRS and INRIA Saclay project team – is interested in learning models from data, focusing on four fundamental axes.

• The first one concerns adversarial learning based on the interaction of two or more learning agents, replacing the unknown objective function by a min-max approach (game theory); this axis is also crucial for the validation and certification of neural networks.
• The second one deals with the selection and configuration of algorithms a priori, according to the available data, also called AutoML; this is not only a necessary condition for the democratization of AI, but also an open challenge for the last 40 years, related to the definition of the data order parameters.
• The third one tackles the problems of learning complex models and deals with the identification of regularities allowing the well-founded augmentation of data in the many application domains where data are small compared to the complexity of the models sought.
• Finally, far from replacing knowledge by models purely derived from data, one objective is to interact with the knowledge of the domain, for example expressed by partial differential equations; the challenge here is to bridge the gap between Learning and Numerical Engineering, in connection with the Department of Fluid Mechanics-Energy