Séminaire Donnees et APprentissage ArtificielRSS

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


27/11/2014
Intervenant(s) : Aurélien Bellet (Télécom ParisTech)
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.
**Bio**
Aurélien Bellet is currently a postdoc at Télécom ParisTech. Previously, he worked as a postdoc at the University of Southern California and received his Ph.D. from the University of Saint-Etienne in 2012. His main research topic is statistical machine learning, with particular interests in metric/similarity learning and large-scale/distributed learning.
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benjamin.piwowarski (at) nulllip6.fr
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