Hi! PARIS is pleased to propose an exceptional seminar of Masashi Sugiyama, RIKEN AIP / The University of Tokyo and member of the Scientific Advisory Board of Hi! PARIS.
We are delighted to welcome Masashi Sugiyama for an exceptional seminar, entitled “Recent advances in robust machine learning”.
ENSAE Paris, Palaiseau (amphi 250)
Recent advances in robust machine learning
When machine learning systems are trained and deployed in the real world, we face various types of uncertainty. For example, training data at hand may contain insufficient information, label noise, and bias. In this talk, I will give an overview of our recent advances in robust machine learning, including weakly supervised classification (positive-unlabeled classification, positive-confidence classification, complementary-label classification, etc), noisy label learning (noise transition estimation, instance-dependent noise, clean sample selection, etc.), and domain adaptation (joint importance-predictor learning for covariate shift adaptation, dynamic importance-predictor learning for full distribution shift, etc.).