Title: Knockoff-Inspired Feature Selection via Generative Models. Abstract: We propose a feature selection algorithm for supervised learning inspired by the recently introduced 
knockoff framework for variable selection in statistical regression. While variable selection in statistics aims 
to distinguish between true and false predictors, feature selection in machine learning aims to reduce the 
dimensionality of the data while preserving the performance of the learning method. The knockoff framework 
has attracted significant interest due to its strong control of false discoveries while preserving predictive 
power. In contrast to the original approach and later variants that assume a given probabilistic model for the 
variables, our proposed approach relies on data-driven generative models that learn mappings from data 
space to a parametric space that characterizes the probability distribution of the data. Our approach 
requires only the availability of mappings from data space to a distribution in parametric space and from 
parametric space to a distribution in data space; thus, it can be integrated with multiple popular generative 
models from machine learning. We provide example knockoff designs using a variational autoencoder and 
a Gaussian process latent variable model. We also propose a knockoff score metric for a softmax classifier 
that accounts for the contribution of each feature and its knockoff during supervised learning. Experimental 
results with multiple benchmark datasets for feature selection showcase the advantages of our knockoff 
designs and the knockoff framework with respect to existing approaches.