Recent advances in weakly-supervised learning and reliable learning by Prof. Masashi Sugiyama
Event details
| Date | 28.05.2019 |
| Hour | 11:00 › 12:00 |
| Speaker | Prof. Masashi Sugiyama, Director of RIKEN Center for Advanced Intelligence Project and Professor at the University of Tokyo http://www.ms.k.u-tokyo.ac.jp/sugi/ |
| Location | |
| Category | Public Science Events |
Abstract:
In this talk, I will introduce our recent research on weakly-supervised learning and reliable learning.
The motivation for weakly-supervised learning is to accurately perform machine learning only from "weak" data that can be collected more easily/cheaply than fully-labeled data. In the first half of this talk, I give an overview of our recently developed empirical risk minimization framework for weakly-supervised classification, covering binary classification only from PU data, PNU data, Pconf data, UU data, SU data, and Comp data (P:positive, N:negative, U:unlabeled, Conf:confidence, S:similar, and Comp:complementary).
For reliable deployment of machine learning systems in the real world, various types of robustness is needed. In the latter half of this talk, I will give an overview of our recent work on robust learning towards noisy training data, changing environments, and adversarial test input.
Finally, I will briefly introduce our RIKEN Center for Advanced Intelligence Project (AIP), which is a national AI project in Japan started in 2016. AIP covers a wide range of topics from generic AI research (machine learning, optimization, applied math., etc.), goal-oriented AI research (material, disaster, cancer, etc.), and AI-in-society research (ethics, data circulation, laws, etc.).
Biography:
Masashi Sugiyama received the PhD degree in Computer Science from Tokyo Institute of Technology, Japan in 2001. He has been Professor at the University of Tokyo since 2014 and concurrently appointed as Director of RIKEN Center for Advanced Intelligence Project in 2016. His research interests include theory, algorithms, and applications of machine learning. He (co)-authored several books such as Density Ratio Estimation in Machine Learning (Cambridge University Press, 2012), Machine Learning in Non-Stationary Environments (MIT Press, 2012), Statistical Reinforcement Learning (Chapman and Hall, 2015), and Introduction to Statistical Machine Learning (Morgan Kaufmann, 2015). He served as a Program co-chair and General co-chair of the Neural Information Processing Systems conference in 2015 and 2016, and as a Program co-chair for the AISTATS conference in 2019. Masashi Sugiyama received the Japan Society for the Promotion of Science Award and the Japan Academy Medal in 2017.
In this talk, I will introduce our recent research on weakly-supervised learning and reliable learning.
The motivation for weakly-supervised learning is to accurately perform machine learning only from "weak" data that can be collected more easily/cheaply than fully-labeled data. In the first half of this talk, I give an overview of our recently developed empirical risk minimization framework for weakly-supervised classification, covering binary classification only from PU data, PNU data, Pconf data, UU data, SU data, and Comp data (P:positive, N:negative, U:unlabeled, Conf:confidence, S:similar, and Comp:complementary).
For reliable deployment of machine learning systems in the real world, various types of robustness is needed. In the latter half of this talk, I will give an overview of our recent work on robust learning towards noisy training data, changing environments, and adversarial test input.
Finally, I will briefly introduce our RIKEN Center for Advanced Intelligence Project (AIP), which is a national AI project in Japan started in 2016. AIP covers a wide range of topics from generic AI research (machine learning, optimization, applied math., etc.), goal-oriented AI research (material, disaster, cancer, etc.), and AI-in-society research (ethics, data circulation, laws, etc.).
Biography:
Masashi Sugiyama received the PhD degree in Computer Science from Tokyo Institute of Technology, Japan in 2001. He has been Professor at the University of Tokyo since 2014 and concurrently appointed as Director of RIKEN Center for Advanced Intelligence Project in 2016. His research interests include theory, algorithms, and applications of machine learning. He (co)-authored several books such as Density Ratio Estimation in Machine Learning (Cambridge University Press, 2012), Machine Learning in Non-Stationary Environments (MIT Press, 2012), Statistical Reinforcement Learning (Chapman and Hall, 2015), and Introduction to Statistical Machine Learning (Morgan Kaufmann, 2015). He served as a Program co-chair and General co-chair of the Neural Information Processing Systems conference in 2015 and 2016, and as a Program co-chair for the AISTATS conference in 2019. Masashi Sugiyama received the Japan Society for the Promotion of Science Award and the Japan Academy Medal in 2017.
Practical information
- Informed public
- Free