Masashi Sugiyama 个人学术档案

引用次数

	总计	2019 年至今
引用	37852	26313
h 指数	91	73
i10 指数	390	286

7000

3500

1750

5250

200720082009201020112012201320142015201620172018201920202021202220232024142 201 371 574 627 1019 1136 1111 1240 1285 1390 1858 2335 3332 4685 5303 6163 4483

开放获取的出版物数量

查看全部

76 篇文章

0 篇文章*

可查看的文章

无法查看的文章

根据资助方的强制性开放获取政策

合著作者

Gang NiuRIKEN Center for Advanced Intelligence Project在 riken.jp 的电子邮件经过验证
Bo HanHKBU / RIKEN在 comp.hkbu.edu.hk 的电子邮件经过验证
Taiji SuzukiThe University of Tokyo在 mist.i.u-tokyo.ac.jp 的电子邮件经过验证
Takafumi KanamoriTokyo Institute of Technology在 c.titech.ac.jp 的电子邮件经过验证
Issei SatoUniversity of Tokyo在 g.ecc.u-tokyo.ac.jp 的电子邮件经过验证
Makoto YamadaOIST & FlatMinima Inc.在 oist.jp 的电子邮件经过验证
Shinichi NakajimaMachine Learning Group, Technische Universität Berlin在 tu-berlin.de 的电子邮件经过验证
Motoaki KawanabeSenior Researcher, Advanced Telecommunications Research Institute International在 atr.jp 的电子邮件经过验证
Hisashi KashimaProfessor, Kyoto University在 i.kyoto-u.ac.jp 的电子邮件经过验证
Tongliang LiuDirector, Sydney AI Centre, University of Sydney & Mohamed bin Zayed University of AI在 sydney.edu.au 的电子邮件经过验证
Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, Germany在 tu-berlin.de 的电子邮件经过验证
Miao XuUniversity of Queensland, RIKEN AIP在 uq.edu.au 的电子邮件经过验证
Ivor W. Tsang, IEEE FellowInaugural A*STAR AI Fellow and Director, Centre for Frontier AI Research, A*STAR在 cfar.a-star.edu.sg 的电子邮件经过验证
Jingfeng ZhangLecturer@University of Auckland & Scientist@RIKEN AIP在 riken.jp 的电子邮件经过验证
Voot TangkarattResearch scientist at Sony AI在 sony.com 的电子邮件经过验证
Song LiuAssociate Professor, University of Bristol, UK在 bristol.ac.uk 的电子邮件经过验证
Ryota TomiokaMicrosoft Research Cambridge在 microsoft.com 的电子邮件经过验证
Nontawat CharoenphakdeeResearcher, Preferred Networks在 preferred.jp 的电子邮件经过验证
Tomoya SakaiIBM Research - Tokyo在 ibm.com 的电子邮件经过验证
Ichiro TakeuchiProfessor, Nagoya University在 mae.nagoya-u.ac.jp 的电子邮件经过验证

关注

Masashi Sugiyama

其他姓名杉山将

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

在 k.u-tokyo.ac.jp 的电子邮件经过验证 - 首页

Machine Learning Data Mining Artificial Intelligence


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Dataset shift in machine learning J Quiñonero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence Mit Press, 2022	2489	2022
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	2183	2018
Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. M Sugiyama Journal of machine learning research 8 (5), 2007	1340	2007
Covariate shift adaptation by importance weighted cross validation. M Sugiyama, M Krauledat, KR Müller Journal of Machine Learning Research 8 (5), 2007	1100	2007
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	1060	2007
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	804	2019
Density ratio estimation in machine learning M Sugiyama, T Suzuki, T Kanamori Cambridge University Press, 2012	690	2012
A least-squares approach to direct importance estimation T Kanamori, S Hido, M Sugiyama The Journal of Machine Learning Research 10, 1391-1445, 2009	612	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	606	2013
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	542	2017
Machine learning in non-stationary environments: Introduction to covariate shift adaptation M Sugiyama, M Kawanabe MIT press, 2012	541	2012
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	531	2017
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	509	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, 699-746, 2008	497	2008
Local fisher discriminant analysis for supervised dimensionality reduction M Sugiyama Proceedings of the 23rd international conference on Machine learning, 905-912, 2006	477	2006
Analysis of learning from positive and unlabeled data MC Du Plessis, G Niu, M Sugiyama Advances in neural information processing systems 27, 2014	439	2014
Attacks which do not kill training make adversarial learning stronger J Zhang, X Xu, B Han, G Niu, L Cui, M Sugiyama, M Kankanhalli International conference on machine learning, 11278-11287, 2020	427	2020
Active learning in recommender systems N Rubens, M Elahi, M Sugiyama, D Kaplan Recommender systems handbook, 809-846, 2015	403	2015
Are anchor points really indispensable in label-noise learning? X Xia, T Liu, N Wang, B Han, C Gong, G Niu, M Sugiyama Advances in neural information processing systems 32, 2019	376	2019
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	372	2014

系统目前无法执行此操作，请稍后再试。

文章 1–20

每年引用数

重复的引用

合并的引用

添加合著者合著作者

上传 PDF

关注此作者

引用次数

合著作者

引用