Overcoming Distribution Shifts: Towards More Flexible and Adaptive Approaches

A fundamental assumption in standard machine learning is that the training data follow the same probability distribution as the test data. However, in many real-world applications, this assumption is frequently violated due to factors such as evolving environments over time or sample selection bias driven by privacy concerns. This phenomenon, known as distribution shift, poses a significant challenge that must be addressed. In this talk, I will provide an overview of our research on tackling distribution shift, covering topics such as covariate shift, joint shift, sequential shift, and out-of-distribution adaptation.

Masashi Sugiyama received his Ph.D. in Computer Science from Tokyo Institute of Technology, Japan, in 2001. After serving as an assistant and associate professor at the same institute, he became a professor at the University of Tokyo in 2014. Since 2016, he has also served as the director of the RIKEN Center for Advanced Intelligence Project. His research interests include theories and algorithms of machine learning such as weakly supervised learning, noise-robust learning, and transfer learning. He was awarded the Japan Academy Medal in 2017 and the Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology of Japan in 2022.