Online Program Home
My Program

Abstract Details

Activity Number: 103 - New Developments on Statistical Machine Learning
Type: Invited
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #300569 Presentation
Title: Deep Knockoffs Machines
Author(s): Emmanuel Candes* and Yaniv Romano and Matteo Sesia
Companies: Stanford University and Stanford University and Stanford University
Keywords: Knockoffs; Deep generative models; Variable selection; False discovery rate

Model-X knockoffs is a new tool for controlling the false discovery rate in very complex statistical models; in fact, they may be so complex that they can be treated as black boxes. To leverage the full power of this framework, however, we need flexible tools to construct knockoffs from sampled data. This talk presents a practical knockoff sampling machine that can produce knockoffs for arbitrary and unspecified data distributions, using deep generative models. The main idea is to iteratively refine a knockoff sampling mechanism until a criterion measuring the validity of the knockoffs we produce is minimized. This criterion measures a distance to pairwise exchangeability and is inspired by a popular maximum mean discrepancy measures in machine learning. Numerical experiments and quantitative tests indicate that our approach yields reasonable knockoffs. Our methods yield a model-free approach, and we present an application to the study of mutations linked to changes in drug resistance in the human immunodeficiency virus.

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

Back to the full JSM 2019 program