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Special CMX Seminar

Friday, October 22, 2021
4:00pm to 5:00pm
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Annenberg 104
Computing Wasserstein barycenters: easy or hard?
Jason Altschuler, Final year PhD Student, Electrical Engineering and Computer Science, Massachusetts Institute of Technology,

A major effort in modern data science is interpreting and extracting geometric information from data. In this talk, I'll focus on my recent work on the core algorithmic task of averaging data distributions. Wasserstein barycenters (aka Optimal Transport barycenters) provide a natural approach for this problem and are central to diverse applications in machine learning, statistics, and computer graphics. Despite considerable attention, it remained unknown whether Wasserstein barycenters can be computed in polynomial time. Our recent work provides a complete answer to this question and reveals that the answer depends subtly on the dimension due to the continuous nature of the problem.

Joint work with Enric Boix-Adsera

For more information, please contact Jolene Brink by phone at 6263952813 or by email at jbrink@caltech.edu or visit Zoom Link to watch virtually!.