[Theory] NOW: [Talks at TTIC] 3/31 TTIC Colloquium: Masashi Sugiyama​, RIKEN/The University of Tokyo

[Brandie Jones via Theory]

*When:*        Monday, March 31st at *11:30am CT*
*Where:       *Talk will be given *live, in-person* at

                       TTIC, 6045 S. Kenwood Avenue

                       5th Floor, Room 530


*Virtually:*  via Panopto (livestream
<https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=3436bdc7-3be8-40f9-a8a3-b248011f659c>
)


*Who: *         Masashi Sugiyama, RIKEN/The University of Tokyo

*Title:*          Machine Learning from Weak, Noisy, and Biased Supervision
*Abstract:  *In many machine learning applications, it is often challenging
to collect a large amount of high-quality labeled data. However, learning
from unlabeled data is not necessarily reliable. To overcome this problem,
the use of imperfect data is promising. In this talk, I will review our
recent research on reliable machine learning from imperfect supervision,
including weakly supervised learning, noisy label learning, and transfer
learning. Finally, I will discuss how machine learning research should
evolve in the era of large foundation models.

*Short Bio*: Masashi Sugiyama received his Ph.D. in Computer Science from
Tokyo Institute of Technology, Japan, in 2001. After serving as an
assistant and associate professor at the same institute, he became a
professor at the University of Tokyo in 2014. Since 2016, he has also
served as the director of the RIKEN Center for Advanced Intelligence
Project. His research interests include theories and algorithms of machine
learning. He was awarded the Japan Academy Medal in 2017 and the
Commendation for Science and Technology by the Minister of Education,
Culture, Sports, Science and Technology of Japan in 2022.

https://scholar.google.co.jp/citations?user=GkYIrlIAAAAJ

*Host: Nati Srebro <nati at ttic.edu>*

--
*Brandie Jones *
*Executive **Administrative Assistant*
Toyota Technological Institute
6045 S. Kenwood Avenue
Chicago, IL  60637
www.ttic.edu
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