Masashi Sugiyama

Cited by

	All	Since 2016
Citations	17791	10607
h-index	65	48
i10-index	248	173

3500

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875

2625

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Co-authors

Gang NiuRIKEN Center for Advanced Intelligence ProjectVerified email at postman.riken.jp
Taiji SuzukiThe University of TokyoVerified email at mist.i.u-tokyo.ac.jp
Takafumi KanamoriTokyo Institute of TechnologyVerified email at c.titech.ac.jp
Hisashi KashimaProfessor, Kyoto UniversityVerified email at i.kyoto-u.ac.jp
Motoaki KawanabeSenior Researcher, Advanced Telecommunications Research Institute InternationalVerified email at atr.jp
Makoto YamadaKyoto University / RIKEN Center for Advanced Intelligence ProjectVerified email at i.kyoto-u.ac.jp
Shinichi NakajimaMachine Learning Group, Technische Universität BerlinVerified email at tu-berlin.de
Klaus-Robert MüllerGoogle Brain (on leave of Professor for Machine Learning, TU Berlin & Korea University, Seoul)Verified email at tu-berlin.de
Bo HanHKBU / RIKENVerified email at comp.hkbu.edu.hk
Neil D. LawrenceUniversity of CambridgeVerified email at cl.cam.ac.uk
Song LiuLecturer in Statistical Science, University of Bristol, UKVerified email at bristol.ac.uk
Joaquin Quiñonero CandelaEngineering Director at FacebookVerified email at fb.com
Ryota TomiokaMicrosoft Research CambridgeVerified email at microsoft.com
Takeuchi IchiroProfessor of Computer Science, Nagoya Institute of TechnologyVerified email at nitech.ac.jp
Tomoya SakaiNEC CorporationVerified email at nec.com
Voot TangkarattPostdoc researcher @ RIKEN AIP, Japan.Verified email at ms.k.u-tokyo.ac.jp
Ivor W. TsangResearch Director & Professor, Australian AI Institute, University of Technology SydneyVerified email at uts.edu.au
Miao XuRIKEN, AIPVerified email at lamda.nju.edu.cn
Neil RubensStanford UniversityVerified email at stanford.edu
Jun SeseHumanome Lab Inc.Verified email at humanome.jp

Masashi Sugiyama

Director, RIKEN Center for Advanced Intelligence Project / Professor, The University of Tokyo

Verified email at k.u-tokyo.ac.jp - Homepage

Machine Learning Data Mining Artificial Intelligence


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Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. M Sugiyama Journal of machine learning research 8 (5), 2007	1091	2007
Dataset shift in machine learning J Quionero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence The MIT Press, 2009	918	2009
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation. M Sugiyama, S Nakajima, H Kashima, P Von Buenau, M Kawanabe NIPS 7, 1433-1440, 2007	712	2007
Covariate shift adaptation by importance weighted cross validation. M Sugiyama, M Krauledat, KR Müller Journal of Machine Learning Research 8 (5), 2007	645	2007
A least-squares approach to direct importance estimation T Kanamori, S Hido, M Sugiyama The Journal of Machine Learning Research 10, 1391-1445, 2009	403	2009
Local fisher discriminant analysis for supervised dimensionality reduction M Sugiyama Proceedings of the 23rd international conference on Machine learning, 905-912, 2006	388	2006
Change-point detection in time-series data by relative density-ratio estimation S Liu, M Yamada, N Collier, M Sugiyama Neural Networks 43, 72-83, 2013	385	2013
Density ratio estimation in machine learning M Sugiyama, T Suzuki, T Kanamori Cambridge University Press, 2012	333	2012
Co-teaching: Robust training of deep neural networks with extremely noisy labels B Han, Q Yao, X Yu, G Niu, M Xu, W Hu, I Tsang, M Sugiyama arXiv preprint arXiv:1804.06872, 2018	311	2018
Semi-supervised local Fisher discriminant analysis for dimensionality reduction M Sugiyama, T Idé, S Nakajima, J Sese Machine learning 78 (1), 35-61, 2010	282	2010
Direct importance estimation for covariate shift adaptation M Sugiyama, T Suzuki, S Nakajima, H Kashima, P von Bünau, ... Annals of the Institute of Statistical Mathematics 60 (4), 699-746, 2008	280	2008
Machine learning in non-stationary environments: Introduction to covariate shift adaptation M Sugiyama, M Kawanabe MIT press, 2012	279	2012
Active learning in recommender systems N Rubens, M Elahi, M Sugiyama, D Kaplan Recommender systems handbook, 809-846, 2015	270	2015
Change-point detection in time-series data by direct density-ratio estimation Y Kawahara, M Sugiyama Proceedings of the 2009 SIAM International Conference on Data Mining, 389-400, 2009	195	2009
When training and test sets are different: characterizing learning transfer A Storkey Dataset shift in machine learning 30, 3-28, 2009	195	2009
Statistical outlier detection using direct density ratio estimation S Hido, Y Tsuboi, H Kashima, M Sugiyama, T Kanamori Knowledge and information systems 26 (2), 309-336, 2011	194	2011
Analysis of Learning from Positive and Unlabeled Data. MC du Plessis, G Niu, M Sugiyama NIPS, 703-711, 2014	178	2014
High-dimensional feature selection by feature-wise kernelized lasso M Yamada, W Jitkrittum, L Sigal, EP Xing, M Sugiyama Neural computation 26 (1), 185-207, 2014	173	2014
Sequential change‐point detection based on direct density‐ratio estimation Y Kawahara, M Sugiyama Statistical Analysis and Data Mining: The ASA Data Science Journal 5 (2 …, 2012	173	2012
Positive-unlabeled learning with non-negative risk estimator R Kiryo, G Niu, MC Plessis, M Sugiyama arXiv preprint arXiv:1703.00593, 2017	168	2017

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