The Department of Applied Mathematics weekly seminar is given by scholars and researchers working in applied mathematics, broadly interpreted.
Title: Provably Robust Machine Learning through Structure-Aware Computation
Abstract: Standard machine learning (ML) algorithms exhibit catastrophic failures when subject to uncertainties in their input data, such as attacks generated by an adversary. Robustness against such uncertainties must be guaranteed in order to reliably deploy ML in safety-critical settings, such as aviation, autonomous driving, and healthcare. This talk presents recent theoretical and computational advancements in provably robust machine learning. We both introduce novel ML models endowed with mathematical proof of robustness, as well as optimization methods to certify the robustness of prior models. By exploiting key structures in the underlying certification problems, the proposed methods achieve state-of-the-art robustness and efficiency.