Talk by Mavashi Sugiyama, RIKEN/University of Tokyo

Title:
Machine learning from weak supervision --- Towards accurate
classification with low labeling costs.

Abstract:
Recent advances in machine learning with big labeled data allow us to
achieve human-level performance in various tasks such as speech
recognition, image understanding, and natural language translation. On
the other hand, there are still many application domains where human
labor is involved in the data acquisition process and thus the use of
massive labeled data is prohibited. In this talk, I will introduce our recent advances in classification techniques from weak supervision, including classification from two sets of unlabeled data, classification from positive and unlabeled data, and a novel approach to semi-supervised classification.

Bio:
Masashi Sugiyama received the PhD degree in Computer Science from
Tokyo Institute of Technology, Japan in 2001. He has been Professor at
the University of Tokyo since 2014 and concurrently appointed as
Director of RIKEN Center for Advanced Intelligence Project in
2016.

His research interests include theory, algorithms, and
applications of machine learning. He (co)-authored several books such
as Density Ratio Estimation in Machine Learning (Cambridge University
Press, 2012), Machine Learning in Non-Stationary Environments (MIT
Press, 2012), Statistical Reinforcement Learning (Chapman and Hall,
2015), and Introduction to Statistical Machine Learning (Morgan
Kaufmann, 2015).

He served as a Program Co-chair and General Co-chair for the Neural Information Processing Systems conference in 2015 and 2016, respectively, and he will be a Program Co-chair for AISTATS2019.
Masashi Sugiyama received the Japan Society for the Promotion of
Science Award and the Japan Academy Medal in 2017.