Fullscreen
Loading...
 
Tao
Print

Seminar17022015

February 17th

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


AurĂ©lien 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.


Contact: cyril.furtlehner at inria.fr


Contributors to this page: furtlehn .
Page last modified on Wednesday 18 of February, 2015 10:11:54 CET by furtlehn.