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TZID:Europe/Paris
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BEGIN:VEVENT
UID:8196@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20150109T140000
DTEND;TZID=Europe/Paris:20150109T150000
DTSTAMP:20241120T210103Z
URL:https://www.i2m.univ-amu.fr/evenements/a-bellet-telecom-paris-the-fran
 k-wolfe-algorithm-recent-results-and-applications-to-high-dimensional-simi
 larity-learning-and-distributed-optimization/
SUMMARY: (...): A. Bellet (Télécom Paris): The Frank-Wolfe Algorithm: Rec
 ent Results and Applications to High-Dimensional Similarity Learning and D
 istributed Optimization
DESCRIPTION:: Title: The Frank-Wolfe Algorithm: Recent Results and Applicat
 ions 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 ove
 r 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 revi
 ew some recent results that motivate its appeal in the context of large-sc
 ale learning problems. In the second part\\\, I will describe two applicat
 ions 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
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TZID:Europe/Paris
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DTSTART:20141026T020000
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