Mathematical Sciences Research Institute

Home » Workshop » Schedules » Scaling Optimal Transport for High dimensional Learning

Scaling Optimal Transport for High dimensional Learning

[Moved Online] Hot Topics: Optimal transport and applications to machine learning and statistics May 04, 2020 - May 08, 2020

May 04, 2020 (09:30 AM PDT - 10:30 AM PDT)
Speaker(s): Gabriel Peyré (École Normale Supérieure)
Location: MSRI: Online/Virtual
  • optimal transport

  • Sinkhorn

  • machine learning

Primary Mathematics Subject Classification
Secondary Mathematics Subject Classification

Scaling Optimal Transport For High Dimensional Learning


Optimal transport (OT) has recently gained lot of interest in machine learning. It is a natural tool to compare in a geometrically faithful way probability distributions. It finds applications in both supervised learning (using geometric loss functions) and unsupervised learning (to perform generative model fitting). OT is however plagued by the curse of dimensionality, since it might require a number of samples which grows exponentially with the dimension. In this talk, I will review entropic regularization methods which define geometric loss functions approximating OT with a better sample complexity. More information and references can be found on the website of our book "Computational Optimal Transport" https://optimaltransport.github.io/

Supplements No Notes/Supplements Uploaded
Video/Audio Files

Scaling Optimal Transport For High Dimensional Learning

H.264 Video 928_28383_8314_Scaling_Optimal_Transport_for_High_DImensional_Learning.mp4
Troubles with video?

Please report video problems to itsupport@msri.org.

See more of our Streaming videos on our main VMath Videos page.