Towards Adversarial and Non-Adversarial Robustness of Machine Learning and Signal Processing: Fundamental Limits and Algorithms

Jirong Yi

Abstract: We are currently in a century of data where massive amount of data are collected and processed every day, and machine learning plays a critical role in automatically processing the data and mining useful information from it for making decisions. Despite the wide and successful applications of machine learning in different fields, the robustness of such systems cannot always be guaranteed in the sense that small variations in a certain aspect of them can result in completely wrong decisions. This prevents the applications of machine learning systems in many safety-critical scenarios such as autonomous driving. In this thesis, we push forward the research on the robustness of machine learning systems in both the regression and the classification tasks.