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Optimal Transport for Machine Learning

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  • UserGabriel Peyré — École Normale Superieure
  • ClockFriday 22 November 2019, 14:00-15:00
  • HouseMR12.

If you have a question about this talk, please contact Dr Sergio Bacallado.

Optimal transport (OT) has become a fundamental mathematical tool at the interface between optimization, partial differential equations and probability. It has recently emerged as an important approach to tackle a surprisingly wide range of applications, such as shape registration in medical imaging, structured prediction in supervised learning and the training of deep generative networks. In this talk, I will review an emerging class of numerical approaches for the approximate resolution of OT-based optimization problems. This offers a new perspective to scale OT for high dimensional problems in machine learning. More information and references can be found on the website of our book “Computational Optimal Transport”

This talk is part of the Statistics series.

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