Gang Niu

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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
Tongliang LiuDirector, Sydney AI Centre, University of SydneyVerified email at sydney.edu.au
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
Quanming YaoAssistant Professor, EE Department, Tsinghua UniversityVerified email at tsinghua.edu.cn
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
Mingming GongUniversity of MelbourneVerified email at unimelb.edu.au
Tomoya SakaiIBM Research - TokyoVerified email at ibm.com
Jingfeng ZhangPhD, NUS, Singapore ---> Postdoc, RIKEN-AIP, TokyoVerified email at riken.jp
Lei FengChongqing University / RIKEN Center for Advanced Intelligence ProjectVerified email at cqu.edu.cn
Bo DaiResearch Scientist, Google BrainVerified email at google.com
Xiaobo XiaPh.D. student, The University of SydneyVerified email at uni.sydney.edu.au
Jiangchao YaoShanghai Jiao Tong UniversityVerified email at sjtu.edu.cn
Makoto YamadaKyoto University / RIKEN Center for Advanced Intelligence Project (AIP)Verified email at i.kyoto-u.ac.jp
Nannan WangProfessor, Xidian UniversityVerified email at xidian.edu.cn
Takashi IshidaLecturer, The University of TokyoVerified email at ms.k.u-tokyo.ac.jp
Nan LuThe University of TokyoVerified email at ms.k.u-tokyo.ac.jp
Bo AnNanyang Technological UniversityVerified email at ntu.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	941	2018
How does disagreement help generalization against label corruption? X Yu, B Han, J Yao, G Niu, IW Tsang, M Sugiyama ICML 2019, 2019	315	2019
Positive-unlabeled learning with non-negative risk estimator R Kiryo, G Niu, MC Plessis, M Sugiyama NeurIPS 2017 (oral), 2017	288	2017
Analysis of learning from positive and unlabeled data MC du Plessis, G Niu, M Sugiyama NeurIPS 2014, 2014	272	2014
Convex formulation for learning from positive and unlabeled data MC du Plessis, G Niu, M Sugiyama ICML 2015, 2015	229	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	180*	2017
Geometry-aware instance-reweighted adversarial training J Zhang, J Zhu, G Niu, B Han, M Sugiyama, M Kankanhalli ICLR 2021 (oral), 2021	152	2021
Masking: A new perspective of noisy supervision B Han, J Yao, G Niu, M Zhou, IW Tsang, Y Zhang, M Sugiyama NeurIPS 2018, 2018	149	2018
Analysis and improvement of policy gradient estimation T Zhao, H Hachiya, G Niu, M Sugiyama NeurIPS 2011, 2011	146	2011
Attacks which do not kill training make adversarial learning stronger J Zhang, X Xu, B Han, G Niu, L Cui, M Sugiyama, M Kankanhalli ICML 2020, 2020	145	2020
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	143	2019
Does distributionally robust supervised learning give robust classifiers? W Hu, G Niu, I Sato, M Sugiyama ICML 2018, 2018	143	2018
Semi-supervised classification based on classification from positive and unlabeled data T Sakai, MC du Plessis, G Niu, M Sugiyama ICML 2017, 2017	98	2017
Information-theoretic semi-supervised metric learning via entropy regularization G Niu, B Dai, M Yamada, M Sugiyama ICML 2012, 2012	96	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	91	2016
Part-dependent label noise: Towards instance-dependent label noise X Xia, T Liu, B Han, N Wang, M Gong, H Liu, G Niu, D Tao, M Sugiyama NeurIPS 2020 (spotlight), 2020	82	2020
Learning from complementary labels T Ishida, G Niu, W Hu, M Sugiyama NeurIPS 2017, 2017	82	2017
SIGUA: Forgetting may make learning with noisy labels more robust B Han, G Niu, X Yu, Q Yao, M Xu, IW Tsang, M Sugiyama ICML 2020, 2020	77*	2020
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	76*	2014
Dual T: Reducing estimation error for transition matrix in label-noise learning Y Yao, T Liu, B Han, M Gong, J Deng, G Niu, M Sugiyama NeurIPS 2020, 2020	75	2020

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