[Extra Tea Talk] Masashi Sugiyama (RIKEN / U Tokyo) Thurs Apr 26 10:30AM AA3195
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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PS
(1) Masashi heads the Japanese equivalent of MILA and is interested in having MILA students do visits or internships there (2) he was program chair and general chair of NIPS (2015, 2016) (3) he is very strong in machine learning theory but also cares about socially beneficial applications
2018-04-19 10:53 GMT-04:00 Michael Noukhovitch mnoukhov@gmail.com:
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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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@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.
lisa_seminaires@iro.umontreal.ca
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