Machine learning from weak supervision

Speaker:        Professor Masashi Sugiyama
                RIKEN/University of Tokyo

Title:          "Machine learning from weak supervision"

Date:           Tuesday, 5 June 2018

Time:           11:00am - 12 noon

Venue:          Room 2404 (via lift 17/18), HKUST

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 two sets of unlabeled data, classification from
positive and unlabeled data, and a novel approach to semi-supervised
classification.


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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 for the Neural Information
Processing Systems conference in 2015 and 2016, respectively, and he will
be a Program Co-chair for AISTATS 2019. Masashi Sugiyama received the
Japan Society for the Promotion of Science Award and the Japan Academy
Medal in 2017.