The information geometry of boosting algorithms

The last two decades have seen the birth and boost of a new category of supervised learning algorithms, known as boosting algorithms. This family has gradually appeared as much more pervasive than initially expected, with applications to the induction of virtually any kind of classifier. While the first lenses used to understand the algorithms were essentially grounded in convex optimization, they have been more recently completed by results in information geometry, escaping the traditional Riemannian framework, that help to get a more complete picture of these fascinating algorithmic machineries. The aim of the talk is to present the central geometric part of this picture, which we believe may serve to design more easily new and more efficient boosting algorithms.