Next week we have an extra tea talk presented by Masashi Sugiyama from RIKEN and University of Tokyo giving a talk on Thursday April 26 at 10:30AM in room AA3195 *note the different room*

Want to meet Prof. Sugiyama? Sign up on the doodle

We've only had great talks and I'm positive this one will be great too!
Michael

TITLE Machine learning from weak supervision - Towards accurate
classification with low labeling costs.

KEYWORDS semi-supervised learning, learning with biased data

ABSTRACT
Recent advances in machine learning with big labeled data allow us to
achieve human-level performance in various tasks such as speech
recognition, image understanding, and natural language translation. On
the other hand, there are still many application domains where human
labor is involved in the data acquisition process and thus the use of
massive labeled data is prohibited.  In this talk, I will introduce
our recent advances in classification techniques from weak
supervision, including classification from positive and unlabeled
data, a novel approach to semi-supervised classification,
classification from positive-confidence data, and classification from
complementary labels

BIO

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 for the Neural
Information Processing Systems conference in 2015 and 2016,
respectively, and he will be a Program Co-chair for AISTATS2019.
Masashi Sugiyama received the Japan Society for the Promotion of
Science Award and the Japan Academy Medal in 2017.

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