Section: Overall Objectives

Goals

Modal is a project-team working on today's complex data sets (mixed data, missing data, high-dimensional data), for classical statistical targets (unsupervised learning, supervised learning, regression,...) with approaches relying on the probabilistic framework. This latter can be tackled through both model-based methods (as mixture models for a generic tool) and model-free methods (as probabilistic bounds on empirical quantities). Furthermore, Modal is connected to the real world by applications, typically with biological ones (some members have this skill) but many other ones are also considered since the application coverage of the Modal methodology is very large. It is also important to note that, in return, applications are often real opportunities for initiating academic questioning for the statistician (case of the Bilille platform and some bilateral contracts of the team).

From the academic communities point of view, Modal can be seen as belonging simultaneously to both the statistical learning and machine learning ones, as attested by its publications. Somewhere it is the opportunity to make a bridge between these two stochastic communities around a common but large probabilistic framework.