Large-scale Optimal Transport: Statistics and Computation

Jonathan Weed

(MIT)


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Date: February 13, 2019

Description:

Optimal transport is a concept from probability which has recently seen an explosion of interest in machine learning and statistics as a tool for analyzing high-dimensional data. However, the key obstacle in using optimal transport in practice has been its high statistical and computational cost. In this talk, we show how exploiting different notions of structure can lead to better statistical rates — beating the curse of dimensionality — and state-of-the-art algorithms.




Created: Wednesday, February 13th, 2019