Masashi Sugiyama

Cited by

	All	Since 2015
Citations	16870	11061
h-index	62	50
i10-index	239	179

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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
Neil D. LawrenceUniversity of CambridgeVerified email at cl.cam.ac.uk
Bo HanHKBU / RIKENVerified email at comp.hkbu.edu.hk
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
Klaus-Robert MüllerGoogle Brain (on leave of Professor for Machine Learning, TU Berlin & Korea University, Seoul)Verified email at tu-berlin.de
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
Jun SeseHumanome Lab Inc.Verified email at humanome.jp
Ivor W. TsangResearch Director & Professor, Australian AI Institute, University of Technology SydneyVerified email at uts.edu.au
Neil RubensStanford UniversityVerified email at stanford.edu
Miao XuRIKEN, AIPVerified email at lamda.nju.edu.cn

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	1066	2007
Dataset shift in machine learning J Quionero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence The MIT Press, 2009	778	2009
Direct importance estimation with model selection and its application to covariate shift adaptation M Sugiyama, S Nakajima, H Kashima, P Buenau, M Kawanabe Advances in neural information processing systems 20, 1433-1440, 2007	678	2007
Covariate shift adaptation by importance weighted cross validation M Sugiyama, M Krauledat, KR MÃžller Journal of Machine Learning Research 8 (May), 985-1005, 2007	626	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	395	2009
Local fisher discriminant analysis for supervised dimensionality reduction M Sugiyama Proceedings of the 23rd international conference on Machine learning, 905-912, 2006	380	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	365	2013
Density ratio estimation in machine learning M Sugiyama, T Suzuki, T Kanamori Cambridge University Press, 2012	327	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 Advances in neural information processing systems, 8527-8537, 2018	284	2018
Semi-supervised local Fisher discriminant analysis for dimensionality reduction M Sugiyama, T Idé, S Nakajima, J Sese Machine learning 78 (1-2), 35, 2010	280	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	275	2008
Machine learning in non-stationary environments: Introduction to covariate shift adaptation M Sugiyama, M Kawanabe MIT press, 2012	268	2012
Active learning in recommender systems N Rubens, M Elahi, M Sugiyama, D Kaplan Recommender systems handbook, 809-846, 2015	266	2015
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	190	2011
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	190	2009
Analysis of learning from positive and unlabeled data MC Du Plessis, G Niu, M Sugiyama Advances in neural information processing systems, 703-711, 2014	165	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	164	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	163	2014
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	159	2009
Input-dependent estimation of generalization error under covariate shift M Sugiyama, KR Müller Statistics and Decisions-International Journal Stochastic Methods and Models …, 2005	148	2005

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