February 17th

14:30 , R2014 Digiteo Shannon (660) (see location):

Aurélien Bellet (slides17012015.pdf)

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

Abstract :

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.

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