Machine learning from weak supervision
Warm welcome to attend a seminar by Prof. Masashi Sugiyama from the University of Tokyo / RIKEN Center for Advanced Intelligence Project
Time: Mon 2018-07-09 13.15 - 14.15
Location: Room 304, Teknikringen 14
Participating: Masashi Sugiyama (Professor, RIKEN/University of Tokyo)
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.
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