Masashi Sugiyama

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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
Bo HanHKBU / RIKENVerified email at comp.hkbu.edu.hk
Shinichi NakajimaMachine Learning Group, Technische Universität BerlinVerified email at tu-berlin.de
Hisashi KashimaProfessor, Kyoto UniversityVerified email at i.kyoto-u.ac.jp
Motoaki KawanabeSenior Researcher, Advanced Telecommunications Research Institute InternationalVerified email at atr.jp
Makoto YamadaOkinawa Institute of Science and Technology / Kyoto University / RIKEN Center for AdvancedVerified email at oist.jp
Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & MPIIVerified email at tu-berlin.de
Miao XuUniversity of Queensland, RIKEN AIPVerified email at uq.edu.au
Ivor W. Tsang, IEEE FellowInaugural A*STAR AI Fellow and Director, Centre for Frontier AI Research, A*STARVerified email at ihpc.a-star.edu.sg
Song LiuSenior Lecturer in Statistical Science, University of Bristol, UKVerified email at bristol.ac.uk
Ryota TomiokaMicrosoft Research CambridgeVerified email at microsoft.com
Takeuchi IchiroProfessor, Nagoya UniversityVerified email at mae.nagoya-u.ac.jp
Tongliang LiuDirector, Sydney AI Centre, University of SydneyVerified email at sydney.edu.au
Voot TangkarattResearch scientist at Sony AIVerified email at sony.com
Tomoya SakaiIBM Research - TokyoVerified email at ibm.com
Nontawat CharoenphakdeeResearcher, Preferred NetworksVerified email at preferred.jp
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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Dataset shift in machine learning J Quinonero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence Mit Press, 2008	1574	2008
Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. M Sugiyama Journal of machine learning research 8 (5), 2007	1232	2007
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 31, 2018	1060	2018
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, 2007	859	2007
Covariate shift adaptation by importance weighted cross validation. M Sugiyama, M Krauledat, KR Müller Journal of Machine Learning Research 8 (5), 2007	850	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	510	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	498	2013
Density ratio estimation in machine learning M Sugiyama, T Suzuki, T Kanamori Cambridge University Press, 2012	483	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	441	2009
Local fisher discriminant analysis for supervised dimensionality reduction M Sugiyama Proceedings of the 23rd international conference on Machine learning, 905-912, 2006	434	2006
Machine learning in non-stationary environments: Introduction to covariate shift adaptation M Sugiyama, M Kawanabe MIT press, 2012	393	2012
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	386	2008
How does disagreement help generalization against label corruption? X Yu, B Han, J Yao, G Niu, I Tsang, M Sugiyama International Conference on Machine Learning, 7164-7173, 2019	346	2019
Advances in neural information processing systems 28 C Cortes, ND Lawarence, DD Lee, M Sugiyama, R Garnett NIPS 2015, 2015	346*	2015
Active learning in recommender systems N Rubens, M Elahi, M Sugiyama, D Kaplan Recommender systems handbook, 809-846, 2015	328	2015
Learning discrete representations via information maximizing self-augmented training W Hu, T Miyato, S Tokui, E Matsumoto, M Sugiyama International conference on machine learning, 1558-1567, 2017	327	2017
Positive-unlabeled learning with non-negative risk estimator R Kiryo, G Niu, MC Du Plessis, M Sugiyama Advances in neural information processing systems 30, 2017	313	2017
Semi-supervised local Fisher discriminant analysis for dimensionality reduction M Sugiyama, T Idé, S Nakajima, J Sese Machine learning 78 (1), 35-61, 2010	306	2010
Analysis of learning from positive and unlabeled data MC Du Plessis, G Niu, M Sugiyama Advances in neural information processing systems 27, 2014	297	2014
Convex formulation for learning from positive and unlabeled data M Du Plessis, G Niu, M Sugiyama International conference on machine learning, 1386-1394, 2015	247	2015

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