Events

Jian Huang

University of Iowa
Colloquia
https://homepage.divms.uiowa.edu/~jian/

When

Date: Wednesday, September 15, 2021
Time: 4:00 pm - 5:00 pm
Location: Virtual through Zoom
Jian Huang is a Professor in the Department of Statistics and Actuarial Science at the University of Iowa. His research interests include high-dimensional statistics, machine learning, bioinformatics, statistical genetics and survival analysis. He has published over 150 peer-reviewed papers in the fields of statistics, bioinformatics, machine learning and statistical genetics. He has been recognized as a highly cited researcher by the Web of Science group. He is a fellow of the American Statistical Association and the Institute of Mathematical Statistics.

Abstract: Learning a probability distribution based on a random sample and sampling from a given distribution are two fundamental problems in statistics and machine learning. These two problems have been studied intensively as two separate questions in the literature. In recent years, generative learning approaches such as generative adversarial networks have been proven to be effective in learning distributions of high-dimensional complex data. In this talk, we consider the problems of sampling from conditional and unnormalized distribution and illustrate that learning and sampling are two sides of the same coin in the context of generative learning. The key to the success of such an approach is the power of deep neural networks in approximating high-dimensional functions. Numerical experiments on benchmark image data and simulation from multimodal distributions demonstrate that generative Monte Carlo has the potential to be a useful addition to the methods for learning and sampling.