  Feature engineering has demonstrated substantial utility for many machine
learning workflows, such as in the small data regime or when distribution
shifts are severe. Thus automating this capability can relieve much manual
effort and improve model performance. Towards this, we propose AutoMAN, or
Automated Mask-based Feature Engineering, an automated feature engineering
framework that achieves high accuracy, low latency, and can be extended to
heterogeneous and time-varying data. AutoMAN is based on effectively exploring
the candidate transforms space, without explicitly manifesting transformed
features. This is achieved by learning feature importance masks, which can be
extended to support other modalities such as time series. AutoMAN learns
feature transform importance end-to-end, incorporating a dataset's task target
directly into feature engineering, resulting in state-of-the-art performance
with significantly lower latency compared to alternatives.
