Special CMX Seminar
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