Machine Learning from Weak Supervision - Towards Accurate Classification with Low Labeling Costs
Machine Learning from Weak Supervision - Towards Accurate Classification with Low Labeling Costs [2,025]
2017 / 11 / 23 AM 10:30
Location: 301 - 103
Speaker: Masashi Sugiyama
RIKEN Center for Advanced Intelligence Project / The University of Tokyo
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 as Professor since 2014 and was 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 a Program co-chair and General co-chair of the Neural Information Processing Systems conference in 2015 and 2016, respectively. Masashi Sugiyama received the Japan Society for the Promotion of Science Award and the Japan Academy Medal in 2017.
Machine learning from big training data is achieving 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. Finally, I will briefly introduce the activities of RIKEN Center for Advanced Intelligence Project.