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Machine Learning for Sciences — Revisiting Direct Density-Ratio Estimation

Update:2018-10-12 21:04:41  Click:

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Title: Machine Learning for Sciences — Revisiting Direct Density-Ratio Estimation


Speaker: Makoto Yamada


Time: Tuesday, October 16, 2018, 09:00-11:00


Venue: Multi-function Hall, 2nd Floor, Dongrong Convention Center, Central Campus


Organizer: School of Artificial Intelligence, International Center of Future Science


Abstract


Density ratio estimation is a comprehensive statistical data processing framework, and it includes various statistical data processing tasks such as transfer learning, outlier detection, cross-domain matching, and change-point detection, to name a few. In this talk, I will first present direct density-ratio estimation methods and their applications to transfer learning, outlier detection, change-point detection, and cross-domain object matching. Then, I will introduce our recent results of density-ratio estimation in deep learning and high-dimensional modeling.


Short biography of Dr. Makoto Yamada


Makoto Yamada, Ph.D., is currently an associate professor at Kyoto University in Japan and the head of the RIKEN AIP. He obtained his master's degree in electrical engineering from Colorado State University in 2005, and a doctorate in statistical science from The Graduate University for Advanced Studies in Japan in 2010. He served as a postdoctoral researcher at the Tokyo Institute of Technology, a researcher at the NTT Communications Science Laboratory, and a research scientist at Yahoo! Reseach. His research interests include machine learning, natural language processing, signal processing, and computer vision. In recent years, he has published more than 30 research papers in top conferences and journals. He won the Best Paper Award in WSDM 2016 and published a paper in Cell in 2018.


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