Machine Learning from Weak Supervision
Machine learning with big data is making a great success. However, there are various application domains that prohibit the use of massive labeled data. In this talk, I will introduce our recent advances in classification from weak supervision, including classification from two sets of unlabeled data, classification from positive and unlabeled data, a novel approach to semi-supervised classification, and classification from complementary labels.
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 he has concurrently served as Director of RIKEN Center for Advanced Intelligence Project since 2016. His research interests include theories and algorithms of machine learning (such as covariate shift adaptation, density ratio estimation, and reinforcement learning) and their applications to real-world problems.