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 ZhangPostdoctoral researcher @RIKEN AIPVerified email at riken.jp
Lei FengChongqing University / RIKEN Center for Advanced Intelligence ProjectVerified email at cqu.edu.cn
Xiaobo XiaPh.D. student, The University of SydneyVerified email at uni.sydney.edu.au
Bo DaiResearch Scientist, Google BrainVerified email at google.com
Nannan WangProfessor, Xidian UniversityVerified email at xidian.edu.cn
Jiangchao YaoShanghai Jiao Tong UniversityVerified email at sjtu.edu.cn
Makoto YamadaOkinawa Institute of Science and Technology / Kyoto University / RIKEN Center for AdvancedVerified email at oist.jp
Nan LuThe University of TokyoVerified email at ms.k.u-tokyo.ac.jp
Takashi IshidaLecturer, The 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


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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	992	2018
How does disagreement help generalization against label corruption? X Yu, B Han, J Yao, G Niu, IW Tsang, M Sugiyama ICML 2019, 2019	333	2019
Positive-unlabeled learning with non-negative risk estimator R Kiryo, G Niu, MC Plessis, M Sugiyama NeurIPS 2017 (oral), 2017	298	2017
Analysis of learning from positive and unlabeled data MC du Plessis, G Niu, M Sugiyama NeurIPS 2014, 2014	283	2014
Convex formulation for learning from positive and unlabeled data MC du Plessis, G Niu, M Sugiyama ICML 2015, 2015	237	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	188*	2017
Geometry-aware instance-reweighted adversarial training J Zhang, J Zhu, G Niu, B Han, M Sugiyama, M Kankanhalli ICLR 2021 (oral), 2021	161	2021
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	159	2020
Masking: A new perspective of noisy supervision B Han, J Yao, G Niu, M Zhou, IW Tsang, Y Zhang, M Sugiyama NeurIPS 2018, 2018	156	2018
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	155	2019
Does distributionally robust supervised learning give robust classifiers? W Hu, G Niu, I Sato, M Sugiyama ICML 2018, 2018	155	2018
Analysis and improvement of policy gradient estimation T Zhao, H Hachiya, G Niu, M Sugiyama NeurIPS 2011, 2011	146	2011
Semi-supervised classification based on classification from positive and unlabeled data T Sakai, MC du Plessis, G Niu, M Sugiyama ICML 2017, 2017	100	2017
Information-theoretic semi-supervised metric learning via entropy regularization G Niu, B Dai, M Yamada, M Sugiyama ICML 2012, 2012	97	2012
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	91	2020
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
Learning from complementary labels T Ishida, G Niu, W Hu, M Sugiyama NeurIPS 2017, 2017	89	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	80*	2020
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	78	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

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