October 19, 2021 12:56
EPFL CIS-RIKEN AIP Joint Seminar #2 20211013 thumbnails


EPFL CIS and RIKEN AIP have started a seminar, titled “EPFL CIS – RIKEN AIP Joint Seminar series” from October, 2021.

EPFL is located in Switzerland and is one of the most vibrant and cosmopolitan science and technology institutions. EPFL has both a Swiss and international vocation and focuses on three missions: teaching, research and innovation.

The Center for Intelligent Systems (CIS) at EPFL, a joint initiative of the schools ENAC, IC, SB, STI and SV seeks to advance research and practice in the strategic field of intelligent systems.

RIKEN is Japan’s largest comprehensive research institution renowned for high-quality research in a diverse range of scientific disciplines.

RIKEN Center for Advanced Intelligence Project (AIP) houses more than 40 research teams ranging from fundamentals of machine learning and optimization, applications in medicine, materials, and disaster, to analysis of ethics and social impact of artificial intelligence.

【The 2nd Seminar】

Date and Time: October 13th 5:00pm – 6:00pm(JST)
Venue:Zoom webinar

Language: English

Speaker: Masashi Sugiyama (RIKEN and the University of Tokyo, Japan)

Title: Robust machine learning for reliable deployment

I will give an overview of our recent advances in reliable machine learning, including weakly supervised learning, learning from noisy supervision, and learning under distribution shift. Specifically, for learning from weak supervision, we introduce an empirical risk minimization framework that allows estimation of the classification risk only from weakly supervised data (such as positive-unlabeled data, positive-confidence data, similar-dissimilar data, complementary labels, and partial labels). This framework is fairly general and is compatible with any loss function, classification model, and optimizer. Next, for learning from noisy supervision, we provide loss correction methods based on noise transition estimation and sample selection methods based on the memorization effect of deep learning.
These methods can overcome the limitations of standard noise-robust approaches based on robust statistics and regularization. Finally, for distribution shift, we present a novel covariate shift adaptation method that allows simultaneous learning of the importance weight and predictor and a dynamic importance weighting method that allows mini-batch-wise adaptation for general distribution shift.

Masashi Sugiyama received the PhD degree in Computer Science from Tokyo Institute of Technology, Japan, in 2001. After experiencing assistant and associate professors at the same institute, he became a professor at the University of Tokyo in 2014. Since 2016, he has concurrently served as Director of RIKEN Center for Advanced Intelligence Project. He (co-)authored machine learning monographs including 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), Variational Bayesian Learning Theory (Cambridge University Press, 2019), and Machine Learning from Weak Supervision (MIT Press, in press). He served as a program co-chair for NeurIPS2015 and AISTATS2019.