RIKEN Center for Advanced Intelligence Project / The University of Tokyo
Towards Robust Machine Learning: Weak Supervision, Noisy Labels, and Beyond
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.
Masashi Sugiyama received Doctor of Engineering in Computer Science from Tokyo Institute of Technology, Japan in 2001. Experiencing Assistant Professor and Associate Professor at Tokyo Institute of Technology, he became Professor at the University of Tokyo in 2014. Since 2016, he has been concurrently serving as Director of RIKEN Center for Advanced Intelligence Project. His research interests include theories and algorithms of machine learning. He received the Japan Academy Medal in 2017 for his series of machine learning research. He coauthored Machine Learning in Non-Stationary Environments (MIT Press, 2012), Density Ratio Estimation in Machine Learning (Cambridge University Press, 2012), Statistical Reinforcement Learning (Chapman and Hall, 2015), Introduction to Statistical Machine Learning (Morgan Kaufmann, 2015), and Machine Learning from Weak Supervision (MIT Press, to appear).
Simon Fraser University
Deakin University’s Applied Artificial Intelligence Institute (A²I²)