学术报告会 | Wasserstein information geometric learning

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时间

2019年6月13日下午14:00

地点

交大闵行校区软件大楼5楼人工智能研究院500会议室

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.

个人简介

Wuchen Li 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.