IPAB Seminar - 01/02/2018

Revisiting Direct Density-Ratio Estimation

 

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.

 

Makoto Yamada received the MS degree in electrical engineering from Colorado State University, Fort Collins, in 2005 and the PhD degree in statistical science from The Graduate University for Advanced Studies (SOKENDAI, The Institute of Statistical Mathematics), Tokyo, in 2010. He has held positions as a postdoctoral fellow with the Tokyo Institute of Technology from 2010 to 2012, as a research associate with NTT Communication Science Laboratories from 2012 to 2013, as a research scientist with Yahoo Labs from 2013 to 2015, and as an assistant professor with Kyoto University from 2015 to 2017. Currently, he is a unit leader at RIKEN AIP. His research interests include machine learning and its application to natural language processing, signal processing, and computer vision. He published more than 30 research papers in premium conferences and journals, and won the WSDM 2016 Best Paper Award.