Online Program Home
My Program

Abstract Details

Activity Number: 141 - Statistical Understanding of Deep Learning
Type: Invited
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #300188
Title: Stein Neural Sampler
Author(s): Guang Cheng* and Tianyang Hu and Zixiang Chen and Hanxi Sun and Jincheng Bai and Mao Ye
Companies: Purdue Statistics and Purdue Statistics and Tsinghua Statistics and Purdue Statistics and Purdue Statistics and Purdue Statistics
Keywords: deep learning; stein discrepancy; neural network; sampling

We propose two sampling procedures to produce high-quality samples from a given (un-normalized) probability density. The sampling is achieved by transforming a reference distribution to the target distribution with neural networks, which are trained separately by minimizing two kinds of Stein Discrepancies. Hence, our method is named as Stein neural sampler. Theoretical and empirical results suggest that, compared with traditional sampling schemes, our samplers experience less convergence issues and generate samples instantaneously with statistical guarantee.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program