The TrustML Young Scientist Seminars (TrustML YSS) started from January 28, 2022.
The TrustML YSS is a video series that features young scientists giving talks and discoveries in relation with Trustworthy Machine Learning.
For more information please see the following site.
This network is funded by RIKEN-AIP’s subsidy and JST, ACT-X Grant Number JPMJAX21AF, Japan.
【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.
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 IJCAI Computers and Thought Award, Alfred P. Sloan Research Fellowship, NSF CAREER Award, MIT Technology Review TR-35 Award, Dean’s Award for Excellence in Research, C.W. Gear Outstanding Junior Faculty 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 trustworthy 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.
Her website is http://boli.cs.illinois.edu/
All participants are required to agree with the AIP Seminar Series Code of Conduct.
Please see the URL below.
RIKEN AIP will expect adherence to this code throughout the event. We expect cooperation from all participants to help ensure a safe environment for everybody.
|Date||December 29, 2022 (Thu) 10:00 - 11:00|