|
Activity Number:
|
206
- Machine Learning Methodology
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
|
|
Sponsor:
|
Section on Statistical Learning and Data Science
|
|
Abstract #313599
|
|
|
Title:
|
Adversarial Networks for Robust Estimation
|
|
Author(s):
|
Ziyue Wang* and Zhiqiang Tan
|
|
Companies:
|
Rutgers University-New Brunswick and Rutgers University
|
|
Keywords:
|
robust estimation ;
f-divergence;
Huber's model;
GAN
|
|
Abstract:
|
Consider robust estimation under Huber's contamination model. We develop and study a class of adversarial networks related to recent work on Generative Adversarial Networks to achieve computationally feasible robust estimation of locations and scatter matrices. The methods are constructed by minimizing f-divergence including the reverse Kullback–Leibler divergence and the total variation distance. We provide theory and numerical experiments to support the effectiveness of our methods.
|
Authors who are presenting talks have a * after their name.
