Masashi Sugiyama - Google Scholar Citations

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	All	Since 2015
Citations	14626	8953
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Co-authors

Taiji SuzukiThe University of TokyoVerified email at mist.i.u-tokyo.ac.jp
Gang NiuRIKEN Center for Advanced Intelligence ProjectVerified email at postman.riken.jp
Hisashi KashimaProfessor, Kyoto UniversityVerified email at i.kyoto-u.ac.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
Motoaki KawanabeSenior Researcher, Advanced Telecommunications Research Institute InternationalVerified email at atr.jp
Song LiuLecturer in Statistical Science, University of Bristol, UKVerified email at bristol.ac.uk
Ryota TomiokaMicrosoft Research CambridgeVerified email at microsoft.com
Klaus-Robert MüllerGoogle Brain (on leave of Professor for Machine Learning, TU Berlin & Korea University, Seoul)Verified email at tu-berlin.de
Neil D. LawrenceUniversity of CambridgeVerified email at cl.cam.ac.uk
Joaquin Quiñonero CandelaEngineering Director at FacebookVerified email at fb.com
Takeuchi IchiroProfessor of Computer Science, Nagoya Institute of TechnologyVerified email at nitech.ac.jp
Tomoya SakaiNEC CorporationVerified email at nec.com
Neil RubensStanford UniversityVerified email at stanford.edu
Jun SeseHumanome Lab Inc.Verified email at humanome.jp
Voot TangkarattPostdoc researcher @ RIKEN AIP, Japan.Verified email at ms.k.u-tokyo.ac.jp
Shohei HidoPreferred Networks, Inc.Verified email at preferred.jp
Bo HanHKBU / RIKENVerified email at comp.hkbu.edu.hk
Koji TsudaProfessor, GSFS, The University of TokyoVerified email at k.u-tokyo.ac.jp
Jaak SimmPostdoctoral researcher, University of LeuvenVerified email at esat.kuleuven.be

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 (May), 1027-1061, 2007	1007	2007
Dataset shift in machine learning J Quionero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence The MIT Press, 2009	638	2009
Direct importance estimation with model selection and its application to covariate shift adaptation M Sugiyama, S Nakajima, H Kashima, PV Buenau, M Kawanabe Advances in neural information processing systems, 1433-1440, 2008	613	2008
Covariate shift adaptation by importance weighted cross validation M Sugiyama, M Krauledat, KR MÃžller Journal of Machine Learning Research 8 (May), 985-1005, 2007	547	2007
Local fisher discriminant analysis for supervised dimensionality reduction M Sugiyama Proceedings of the 23rd international conference on Machine learning, 905-912, 2006	358	2006
A least-squares approach to direct importance estimation T Kanamori, S Hido, M Sugiyama Journal of Machine Learning Research 10 (Jul), 1391-1445, 2009	355	2009
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	315	2013
Density ratio estimation in machine learning M Sugiyama, T Suzuki, T Kanamori Cambridge University Press, 2012	283	2012
Semi-supervised local Fisher discriminant analysis for dimensionality reduction M Sugiyama, T Idé, S Nakajima, J Sese Machine learning 78 (1-2), 35, 2010	276	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	247	2008
Active learning in recommender systems N Rubens, M Elahi, M Sugiyama, D Kaplan Recommender systems handbook, 809-846, 2015	239	2015
Machine learning in non-stationary environments: Introduction to covariate shift adaptation M Sugiyama, M Kawanabe MIT press, 2012	232	2012
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	176	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	175	2011
Link propagation: A fast semi-supervised learning algorithm for link prediction H Kashima, T Kato, Y Yamanishi, M Sugiyama, K Tsuda Proceedings of the 2009 SIAM international conference on data mining, 1100-1111, 2009	155	2009
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 Advances in neural information processing systems, 8527-8537, 2018	154	2018
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	147	2012
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	140	2014
Input-dependent estimation of generalization error under covariate shift M Sugiyama, KR Müller Statistics & Decisions 23 (4/2005), 249-279, 2005	140	2005
Active learning in approximately linear regression based on conditional expectation of generalization error M Sugiyama Journal of Machine Learning Research 7 (Jan), 141-166, 2006	128	2006

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