  In this discussion paper, we survey recent research surrounding robustness of
machine learning models. As learning algorithms become increasingly more
popular in data-driven control systems, their robustness to data uncertainty
must be ensured in order to maintain reliable safety-critical operations. We
begin by reviewing common formalisms for such robustness, and then move on to
discuss popular and state-of-the-art techniques for training robust machine
learning models as well as methods for provably certifying such robustness.
From this unification of robust machine learning, we identify and discuss
pressing directions for future research in the area.
