The 47th Seminar
Date and Time: Dec. 29th 10:00 am – 11:00 am(JST)
Venue: Zoom webinar
Speaker: Bo Li (UIUC)
Title: Trustworthy Machine Learning via Learning with Reasoning
Advances in machine learning have led to the rapid and widespread deployment of ML algorithms in safety-critical applications, such as autonomous driving and medical healthcare. Standard machine learning systems, however, assume that training and test data follow the same, or similar, distributions, without explicitly considering active adversaries manipulating either distribution. For instance, recent work has demonstrated that motivated adversaries can circumvent anomaly detection or other machine learning models at test-time through evasion attacks, or can inject well-crafted malicious instances into training data to induce errors during inference through poisoning attacks. Such distribution shift could also lead to other trustworthiness issues such as generalization. In this talk, I will describe different perspectives of trustworthy machine learning, such as robustness, privacy, generalization, and their underlying interconnections. I will focus on a certifiably robust learning approach based on statistical learning with logical reasoning as an example, and then discuss the principles towards designing and developing practical trustworthy machine learning systems with guarantees, by considering these trustworthiness perspectives in a holistic view.