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.

No one likes being reduced to a number. For example, there is much more to my financial picture than my credit score alone. There is even scholarly work on weaknesses in the system to compute this score. Everyone may agree the number is far from perfect, yet it is used to make decisions that matter to me, as Moshe Y. Vardi discussed in his Editor's Letter "Academic Rankings Considered Harmful!" (Sept. So I care what my credit score is.

Humans can perform many tasks with ease that remain difficult or impossible for computers. Crowdsourcing platforms like Amazon Mechanical Turk make it possible to harness human-based computational power at an unprecedented scale, but their utility as a general-purpose computational platform remains limited. The lack of complete automation makes it difficult to orchestrate complex or interrelated tasks. Recruiting more human workers to reduce latency costs real money, and jobs must be monitored and rescheduled when workers fail to complete their tasks. Furthermore, it is often difficult to predict the length of time and payment that should be budgeted for a given task.