报告题目：Wasserstein information geometric learning

报告人： Wuchen Li  Assistant professor  University of California, Los Angeles.

主持人： 查宏远 教授

报告时间：2019年6月14日  周五10:00-11:00  

报告地点：理科大楼A1514

报告摘要：

Optimal transport (Wasserstein metric) nowadays play important roles in data science and machine learning. In this talk, we brief review its development and applications in machine learning. In particular, we will focus its induced differential structure. We will introduce the Wasserstein natural gradient in parametric models. The metric tensor in probability density space is pulled back to the one on parameter space. We derive the Wasserstein gradient flows and proximal operator in parameter space. We demonstrate that the Wasserstein natural gradient works efficiently in several statistical machine learning problems, including Boltzmann machine, generative adversary models (GANs) and variational Bayesian statistics.

报告人简介：

Wuchen Li was from Shandong. He received his BSc in Mathematics  from Shandong university in 2009, and a Ph.D. degree in Mathematics from Georgia institute of Technology in 2016. After then, he is appointed as a CAM assistant professor in University of California, Los Angeles.