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H.B. Keller Colloquim

Monday, May 8, 2023
4:00pm to 5:00pm
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Annenberg 105
Learning Matchings, Maps, and Trajectories
Jonathan Niles-Weed, Assistant Professor of Mathematics and Data Science, Courant Institute of Mathematical Sciences and the Center for Data Science, New York University,

This talk will survey some advances in the use of optimal transport for machine learning problems. Optimal transport considers the geometrical properties of transformations of probability distributions, making it a suitable framework for many applications in sampling, generative modeling, fairness, and causal inference. We will study estimators for this problem, characterizing their finite-sample behavior and obtaining distributional limits suitable for practical inference. Additionally, we will explore structural assumptions that improve the statistical and computational performance of these estimators in high dimensions.

For more information, please contact Diana Bohler by phone at 626-395-1768 or by email at [email protected].