Classification from Weak Supervision

Machine learning from big training data is achieving 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.