[Lisa_seminaires] [Extra Tea Talk] Masashi Sugiyama (RIKEN / U Tokyo) Thurs Apr 26 10:30AM AA3195

Michael Noukhovitch mnoukhov at gmail.com
Ven 27 Avr 09:04:13 EDT 2018


for those who missed this talk, you can find the recording here!
https://bluejeans.com/s/E4PWm

On Thu, Apr 19, 2018 at 10:53 AM Michael Noukhovitch <mnoukhov at gmail.com>
wrote:

> 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
> <https://doodle.com/poll/b48c5gru3e89vpmc>
>
> 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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