Talk by Mavashi Sugiyama, RIKEN/University of Tokyo

Friday, May 4, 2018 | 1:00pm - 2:30pm

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

Recent advan
ces in machine learning with big labeled data allow us to
achieve human-l
evel performance in various tasks such as speech
recognition, image unde
rstanding, and natural language translation. On
the other hand, there a
re still many application domains where human
labor is involved in the da
ta acquisition process and thus the use of
massive labeled data is prohib
ited. In this talk, I will introduce our recent advances in classificatio
n techniques from weak supervision, including classification from two set
s of unlabeled data, classification from positive and unlabeled data, an
d a novel approach to semi-supervised classification.

Masashi S
ugiyama received the PhD degree in Computer Science from
Tokyo Institute
of Technology, Japan in 2001. He has been Professor at
the University ofTokyo since 2014 and concurrently appointed as
Director of RIKEN Center
for Advanced Intelligence Project in

His research interests in
clude theory, algorithms, and
applications of machine learning. He (co)
-authored several books such
as Density Ratio Estimation in Machine Learn
ing (Cambridge University
Press, 2012), Machine Learning in Non-Station
ary Environments (MIT
Press, 2012), Statistical Reinforcement Learning
(Chapman and Hall,
2015), and Introduction to Statistical Machine Learn
ing (Morgan
Kaufmann, 2015).

He served as a Program Co-chair and Ge
neral Co-chair for the Neural Information Processing Systems conference in2015 and 2016, respectively, and he will be a Program Co-chair for AIST
Masashi Sugiyama received the Japan Society for the Promotion of
Science Award and the Japan Academy Medal in 2017.

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