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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” https://optimaltransport.github.io/

This talk is part of the Statistics series.

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