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SUMMARY:Gabriel Peyré (CNRS\, Ecole Normale Supérieure)
DTSTART:20200420T120000Z
DTEND:20200420T124500Z
DTSTAMP:20260423T005742Z
UID:OWMADS/1
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/OWMADS/1/">S
 caling Optimal Transport for High dimensional Learning</a>\nby Gabriel Pey
 ré (CNRS\, Ecole Normale Supérieure) as part of One World seminar: Mathe
 matical Methods for Arbitrary Data Sources (MADS)\n\n\nAbstract\nOptimal t
 ransport (OT) has recently gained lot of interest in machine learning. It 
 is a natural tool to compare in a geometrically faithful way probability d
 istributions. It finds applications in both supervised learning (using geo
 metric loss functions) and unsupervised learning (to perform generative mo
 del fitting). OT is however plagued by the curse of dimensionality\, since
  it might require a number of samples which grows exponentially with the d
 imension. In this talk\, I will review entropic regularization methods whi
 ch define geometric loss functions approximating OT with a better sample c
 omplexity. More information and references can be found on the website of 
 our book Computational Optimal Transport.\n
LOCATION:https://researchseminars.org/talk/OWMADS/1/
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