An Overview and Prospective Outlook on Robust Training and Certification of Machine Learning Models
This is an incremental survey paper that synthesizes existing work on robust training and certification for researchers and practitioners in machine learning and control systems.
The paper surveys recent research on ensuring machine learning model robustness against data uncertainty, particularly for safety-critical applications, and identifies future research directions without presenting new experimental results.
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.