– A. Bellet (Télécom Paris) : The Frank-Wolfe Algorithm : Recent Results and Applications to High-Dimensional Similarity Learning and Distributed Optimization

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Date/heure
Date(s) - 09/01/2015
14 h 00 min - 15 h 00 min

Catégories Pas de Catégories


Title : The Frank-Wolfe Algorithm : Recent Results and Applications to High-Dimensional Similarity Learning and Distributed Optimization\n\nAbstract : 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.\n[