Hi! PARIS - Seminar "Recent advances in robust machine learning" with Masashi Sugiyama
The interdisciplinary center Hi! PARIS (IP Paris/HEC) will welcome Masashi Sugiyama to talk about the recent advances in robust machine learning, including weakly supervised classification, noisy label learning and domain adaptation. The seminar will be in English. It will take place on September 26, 2022 from 2:00 pm to 3:30 pm at ENSAE Paris, Palaiseau, but also available on Zoom.
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.).
Masashi Sugiyama received a Ph.D. in Computer Science from Tokyo Institute of Technology in 2001. He has been a Professor at the University of Tokyo since 2014 and concurrently Director of the RIKEN Center for Advanced Intelligence Project (AIP) since 2016. His research interests include theories and algorithms of machine learning. In 2022, he received the Award for Science and Technology from Japan’s Minister of Education, Culture, Sports, Science, and Technology. He served as Program Co-chairs for Neural Information Processing Systems (NeurIPS) Conference in 2015, International Conference on Artificial Intelligence and Statistics (AISTATS) in 2019, and Asian Conference on Machine Learning (ACML) in 2010 and 2020. He (co)authored Machine Learning in Non-Stationary Environments (MIT Press, 2012), Density Ratio Estimation in Machine Learning (Cambridge University Press, 2012), Statistical Reinforcement Learning (Chapman & Hall, 2015), Introduction to Statistical Machine Learning (Morgan Kaufmann, 2015), and Machine Learning from Weak Supervision (MIT Press, 2022).