Optimal Transport for Machine Learning

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/

• Speaker: Gabriel Peyré — École Normale Superieure
• Friday 22 November 2019, 14:00–15:00
• Venue: MR12.
• Series: Statistics; organiser: Dr Sergio Bacallado.