Abstract

In recent machine learning applications, training data is often full of uncertainties. In this talk, Prof Sugiyama will give an overview of their research on reliable machine learning from imperfect information, including weakly supervised learning, noise-robust learning, and transfer learning.

Then, he will discuss their recent challenges to integrate these approaches and develop a generic machine learning methodology with fewer modeling assumptions.

 

Speaker

Prof Masashi Sugiyama (Director, RIKEN Center for Advanced Intelligence Project,The University of Tokyo) 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.