  Research on adversarial robustness is primarily focused on image and text
data. Yet, many scenarios in which lack of robustness can result in serious
risks, such as fraud detection, medical diagnosis, or recommender systems often
do not rely on images or text but instead on tabular data. Adversarial
robustness in tabular data poses two serious challenges. First, tabular
datasets often contain categorical features, and therefore cannot be tackled
directly with existing optimization procedures. Second, in the tabular domain,
algorithms that are not based on deep networks are widely used and offer great
performance, but algorithms to enhance robustness are tailored to neural
networks (e.g. adversarial training).
  In this paper, we tackle both challenges. We present a method that allows us
to train adversarially robust deep networks for tabular data and to transfer
this robustness to other classifiers via universal robust embeddings tailored
to categorical data. These embeddings, created using a bilevel alternating
minimization framework, can be transferred to boosted trees or random forests
making them robust without the need for adversarial training while preserving
their high accuracy on tabular data. We show that our methods outperform
existing techniques within a practical threat model suitable for tabular data.
