Séminaire (Organisé par l’Equipe de recherche DI)

Aurélien BELLET

Télécom ParisTech

The Frank-Wolfe Algorithm : Recent Results and Applications to High-Dimensional Similarity Learning and Distributed Optimization

Mardi 16 décembre 2014 à 10h30 en salle A108

Résumé :

The topic of this talk is the Frank-Wolfe (FW) algorithm, a greedy procedure for minimizing a convex and differentiable function over a compact convex set. FW finds its roots in the 1950’s but has recently regained a lot of interest in machine learning and related communities. In the first part of the talk, I will introduce the FW algorithm and review some recent results that motivate its appeal in the context of large-scale learning problems. In the second part, I will describe two applications of FW in my own work : (i) learning a similarity/distance function for sparse high-dimensional data, and (ii) learning sparse combinations of elements that are distributed over a network.

PDF - 1.5 Mo


FR SHIC 3272

Collegium UTC/CNRS