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Trustworthy Machine Learning: Robustness, Privacy, Generalization, and their Interconnections

February 23 (2022) @ 3:00 pm - 4:00 pm

Advances in machine learning have led to rapid and widespread deployment of learning based inference and decision making for safety-critical applications, such as autonomous driving and security diagnostics. Current machine learning systems, however, assume that training and test data follow the same, or similar, distributions, and do not consider active adversaries manipulating either distribution. 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 in inference time through poisoning attacks. In this talk, I will describe my recent research about security and privacy problems in machine learning systems, with a focus on potential certifiably defense approaches via logic reasoning and domain knowledge integration with neural networks. We will also discuss other defense principles towards developing practical robust learning systems with robustness guarantees.

Zoom meeting link: https://newcastleuniversity.zoom.us/j/81238177624?pwd=Nm16blNtakgwMmgrVVZpbmNCU2t5Zz09

Meeting ID: 812 3817 7624
Passcode: 485647

Youtube live streaming: https://youtu.be/V5w45Im_O74

Youtube VoD

Details

Date:
February 23 (2022)
Time:
3:00 pm - 4:00 pm
Seminar Tags:
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Presenter

Bo Li (University of Illinois at Urbana-Champaign)

Dr. Bo Li is an assistant professor in the Department of Computer Science at the University of Illinois at Urbana–Champaign. She is the recipient of the MIT Technology Review TR-35 award, Alfred P. Sloan Research Fellowship, NSF CAREER Award, Intel Rising Star award, Symantec Research Labs Fellowship, Rising Star Award, Research Awards from Tech companies such as Amazon, Facebook, Intel, and IBM, and best paper awards at several top machine learning and security conferences. Her research focuses on both theoretical and practical aspects of trustworthy machine learning, security, machine learning, privacy, and game theory. She has designed several scalable frameworks for robust machine learning and privacy-preserving data publishing systems. Her work has been featured by major publications and media outlets such as Nature, Wired, Fortune, and New York Times.

View Presenter Website

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