Title: Machine Learning for Sciences — Revisiting Direct Density-Ratio Estimation
Speaker: Makoto Yamada
Time: 9: 00-11: 00, Tuesday, 16 October, 2018
Venue: Multi-function Hall, 2nd Floor, Dongrong Conference 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.
Biography:
Dr. Makoto Yamada is currently an Associate Professor at Kyoto University and head of RIKEN AIP. He obtained his Master’s Degree in Electrical Engineering from Colorado State University, Fort Collins, the USA in 2005 and his Doctoral Degree in The Graduate University for Advanced Studies, Japan in 2010. He has served as a postdoctoral researcher at the Tokyo Institute of Technology, a researcher at NTT Communication Science Laboratory, and a research scientist at Yahoo Labs. His research areas include Machine Learning, Natural Language Processing, Signal Processing, and Computer Vision. In recent years, more than 30 research papers have been published in top conferences and journals. He won the Best Paper Award of WSDM in 2016 and published a paper in Cell in 2018.
