Gang Niu

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	All	Since 2016
Citations	2364	2176
h-index	23	23
i10-index	39	36

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Co-authors

Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
Bo HanHKBU / RIKENVerified email at comp.hkbu.edu.hk
Ivor W. TsangResearch Director & Professor, Australian AI Institute, University of Technology SydneyVerified email at uts.edu.au
Miao XuRIKEN, AIPVerified email at lamda.nju.edu.cn
Tomoya SakaiNEC CorporationVerified email at nec.com
Quanming YaoSenior Scientist, 4ParadigmVerified email at cse.ust.hk
Tongliang LiuUniversity of Sydney and Visiting Scientist at RIKEN AIPVerified email at sydney.edu.au
Xingrui YuPh.D Student, University of Technology Sydney (UTS)Verified email at student.uts.edu.au
Weihua HuPh.D. student, Stanford UniversityVerified email at stanford.edu
Bo DaiResearch Scientist, Google BrainVerified email at google.com
Makoto YamadaKyoto University / RIKEN Center for Advanced Intelligence ProjectVerified email at i.kyoto-u.ac.jp
Jiangchao YaoAlibaba GroupVerified email at sjtu.edu.cn
Takashi IshidaPh.D. student, The University of TokyoVerified email at ms.k.u-tokyo.ac.jp
Nannan WangProfessor, Xidian UniversityVerified email at xidian.edu.cn
Nan LuPh.D. student, The University of TokyoVerified email at ms.k.u-tokyo.ac.jp
Mingming GongThe University of MelbourneVerified email at unimelb.edu.au
Lei FengChongqing University, ChinaVerified email at cqu.edu.cn
Ryuichi KiryoThe University of TokyoVerified email at ms.k.u-tokyo.ac.jp
Bo AnNanyang Technological UniversityVerified email at ntu.edu.sg
Jingfeng ZhangPhD, National University of SingaporeVerified email at comp.nus.edu.sg

Gang Niu

RIKEN Center for Advanced Intelligence Project

Verified email at postman.riken.jp - Homepage

Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Co-teaching: Robust training of deep neural networks with extremely noisy labels B Han, Q Yao, X Yu, G Niu, M Xu, W Hu, IW Tsang, M Sugiyama NeurIPS 2018, 2018	346	2018
Analysis of learning from positive and unlabeled data MC du Plessis, G Niu, M Sugiyama NeurIPS 2014, 2014	185	2014
Positive-unlabeled learning with non-negative risk estimator R Kiryo, G Niu, MC Plessis, M Sugiyama NeurIPS 2017, 2017	176	2017
Convex formulation for learning from positive and unlabeled data MC du Plessis, G Niu, M Sugiyama ICML 2015, 2015	155	2015
Class-prior estimation for learning from positive and unlabeled data MC du Plessis, G Niu, M Sugiyama Machine Learning 106 (4), 463--492, 2017	139*	2017
Analysis and improvement of policy gradient estimation T Zhao, H Hachiya, G Niu, M Sugiyama NeurIPS 2011, 2011	113	2011
How does disagreement help generalization against label corruption? X Yu, B Han, J Yao, G Niu, IW Tsang, M Sugiyama ICML 2019, 2019	103*	2019
Masking: A new perspective of noisy supervision B Han, J Yao, G Niu, M Zhou, IW Tsang, Y Zhang, M Sugiyama NeurIPS 2018, 2018	81	2018
Information-theoretic semi-supervised metric learning via entropy regularization G Niu, B Dai, M Yamada, M Sugiyama ICML 2012, 2012	81	2012
Theoretical comparisons of positive-unlabeled learning against positive-negative learning G Niu, MC du Plessis, T Sakai, Y Ma, M Sugiyama NeurIPS 2016, 2016	76	2016
Information-maximization clustering based on squared-loss mutual information M Sugiyama, G Niu, M Yamada, M Kimura, H Hachiya Neural Computation 26 (1), 84--131, 2014	72*	2014
Semi-supervised classification based on classification from positive and unlabeled data T Sakai, MC du Plessis, G Niu, M Sugiyama ICML 2017, 2017	71	2017
Does distributionally robust supervised learning give robust classifiers? W Hu, G Niu, I Sato, M Sugiyama ICML 2018, 2018	54	2018
Squared-loss mutual information regularization: A novel information-theoretic approach to semi-supervised learning G Niu, W Jitkrittum, B Dai, H Hachiya, M Sugiyama ICML 2013, 2013	54	2013
Are anchor points really indispensable in label-noise learning? X Xia, T Liu, N Wang, B Han, C Gong, G Niu, M Sugiyama NeurIPS 2019, 2019	50	2019
Learning from complementary labels T Ishida, G Niu, W Hu, M Sugiyama NeurIPS 2017, 2017	47	2017
Classification from pairwise similarity and unlabeled data H Bao, G Niu, M Sugiyama ICML 2018, 2018	40	2018
On the minimal supervision for training any binary classifier from only unlabeled data N Lu, G Niu, AK Menon, M Sugiyama ICLR 2019, 2019	38	2019
Semi-supervised AUC optimization based on positive-unlabeled learning T Sakai, G Niu, M Sugiyama Machine Learning 107 (4), 767--794, 2018	30	2018
Complementary-label learning for arbitrary losses and models T Ishida, G Niu, AK Menon, M Sugiyama ICML 2019, 2019	27	2019

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