  A new statistical procedure (Model-X \cite{candes2018}) has provided a way to
identify important factors using any supervised learning method controlling for
FDR. This line of research has shown great potential to expand the horizon of
machine learning methods beyond the task of prediction, to serve the broader
needs in scientific researches for interpretable findings. However, the lack of
a practical and flexible method to generate knockoffs remains the major
obstacle for wide application of Model-X procedure. This paper fills in the gap
by proposing a model-free knockoff generator which approximates the correlation
structure between features through latent variable representation. We
demonstrate our proposed method can achieve FDR control and better power than
two existing methods in various simulated settings and a real data example for
finding mutations associated with drug resistance in HIV-1 patients.
