KEYNOTE: Towards More Robust and Reliable Machine Learning

Masashi Sugiyama, Riken, The University of Tokyo, JAPAN. ABSTRACT: In statistical machine learning, training data is often full of uncertainties due to insufficient information, label noise, and bias. In this talk, I will give an overview of our research on reliable machine learning, including weakly supervised learning, noise-robust learning, and transfer learning. Then, I will discuss our recent challenges to integrate these approaches and develop a generic machine learning methodology with fewer modeling assumptions.