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
Shinichi NakajimaMachine Learning Group, Technische Universität BerlinVerified email at tu-berlin.de
Bo HanHKBU / RIKENVerified email at comp.hkbu.edu.hk
Hisashi KashimaProfessor, Kyoto UniversityVerified email at i.kyoto-u.ac.jp
Makoto YamadaKyoto University / RIKEN Center for Advanced Intelligence Project (AIP)Verified email at i.kyoto-u.ac.jp
Motoaki KawanabeSenior Researcher, Advanced Telecommunications Research Institute InternationalVerified email at atr.jp
Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & MPIIVerified email at tu-berlin.de
Ryota TomiokaMicrosoft Research CambridgeVerified email at microsoft.com
Miao XuUniversity of Queensland, RIKEN AIPVerified email at uq.edu.au
Takeuchi IchiroProfessor, Nagoya UniversityVerified email at mae.nagoya-u.ac.jp
Song LiuLecturer in Statistical Science, University of Bristol, UKVerified email at bristol.ac.uk
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
Voot TangkarattResearch scientist at Sony AIVerified email at sony.com
Tongliang LiuDirector, Sydney AI Centre, University of SydneyVerified email at sydney.edu.au
Tomoya SakaiNEC CorporationVerified email at nec.com
Neil RubensStanford UniversityVerified email at stanford.edu
Nontawat CharoenphakdeeResearcher, Preferred NetworksVerified email at preferred.jp
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 Quiñonero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence Mit Press, 2008	1369	2008
Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. M Sugiyama Journal of machine learning research 8 (5), 2007	1192	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	857	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	822	2007
Covariate shift adaptation by importance weighted cross validation. M Sugiyama, M Krauledat, KR Müller Journal of Machine Learning Research 8 (5), 2007	815	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	484	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	465	2013
Density ratio estimation in machine learning M Sugiyama, T Suzuki, T Kanamori Cambridge University Press, 2012	441	2012
Local fisher discriminant analysis for supervised dimensionality reduction M Sugiyama Proceedings of the 23rd international conference on Machine learning, 905-912, 2006	430	2006
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	425	2009
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	359	2008
Advances in neural information processing systems 28 C Cortes, ND Lawarence, DD Lee, M Sugiyama, R Garnett NIPS 2015, 2015	358*	2015
Machine learning in non-stationary environments: Introduction to covariate shift adaptation M Sugiyama, M Kawanabe MIT press, 2012	333	2012
Active learning in recommender systems N Rubens, M Elahi, M Sugiyama, D Kaplan Recommender systems handbook, 809-846, 2015	310	2015
Semi-supervised local Fisher discriminant analysis for dimensionality reduction M Sugiyama, T Idé, S Nakajima, J Sese Machine learning 78 (1), 35-61, 2010	300	2010
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	277	2017
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	273	2019
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	273	2017
Analysis of learning from positive and unlabeled data MC Du Plessis, G Niu, M Sugiyama Advances in neural information processing systems 27, 2014	259	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	226	2014

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