Nov 17-2022 SDSA Seminars, Yun Yang: Statistical Learning and Computation via Optimal Transport
Channel: Statistics Doctoral Students Association
Date: 2022-11-17

Yun Yang , Associate Professor Department of Statistics- UIUC : Statistical Learning and Computation via Optimal Transport

Abstract:

Optimal transport (OT) theory has long-standing connections to probability and statistics. In this talk, I will discuss some applications of OT in generative modeling and Bayesian inference. Unlike traditional distances or discrepancy measures over distributions, Wasserstein distances defined via OT can capture closeness between mutually singular probability distributions. Therefore, they are more useful in addressing modern machine learning tasks where data displayed as high-dimensional vectors usually have low-dimensional structures. Another important application of OT concerns the Wasserstein gradient flow approach to solving minimization problems over the space of all probability distributions. This makes OT naturally suitable for computing or approximating posterior distributions in Bayesian models. 

https://sdsa.web.illinois.edu/calendar/research-seminar-sayan-chakrabarty-department-of-statistics-and-yun-yang-department-of-statistics/