Gang Niu

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Coauteurs

Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoAdresse e-mail validée de k.u-tokyo.ac.jp
Bo HanHKBU / RIKENAdresse e-mail validée de comp.hkbu.edu.hk
Tongliang LiuDirector, Sydney AI Centre, University of Sydney & Mohamed bin Zayed University of AIAdresse e-mail validée de sydney.edu.au
Miao XuUniversity of Queensland, RIKEN AIPAdresse e-mail validée de uq.edu.au
Ivor W. Tsang, IEEE FellowInaugural A*STAR AI Fellow and Director, Centre for Frontier AI Research, A*STARAdresse e-mail validée de cfar.a-star.edu.sg
Lei FengNanyang Technological UniversityAdresse e-mail validée de ntu.edu.sg
Jingfeng ZhangLecturer@University of Auckland & Scientist@RIKEN AIPAdresse e-mail validée de riken.jp
Quanming YaoAssistant Professor, EE Department, Tsinghua UniversityAdresse e-mail validée de tsinghua.edu.cn
Mingming GongUniversity of Melbourne & Mohamed bin Zayed University of Artificial IntelligenceAdresse e-mail validée de unimelb.edu.au
Xingrui YuPh.D Student, University of Technology Sydney (UTS)Adresse e-mail validée de student.uts.edu.au
Weihua HuKumo AIAdresse e-mail validée de kumo.ai
Chen GongProfessor@Nanjing University of Science and TechnologyAdresse e-mail validée de njust.edu.cn
Tomoya SakaiIBM Research - TokyoAdresse e-mail validée de ibm.com
Bo AnNanyang Technological UniversityAdresse e-mail validée de ntu.edu.sg
Xiaobo XiaPh.D. student, The University of SydneyAdresse e-mail validée de uni.sydney.edu.au
Jiangchao YaoShanghai Jiao Tong UniversityAdresse e-mail validée de sjtu.edu.cn
Takashi IshidaResearch Scientist, RIKEN AIP / Lecturer, The University of TokyoAdresse e-mail validée de ms.k.u-tokyo.ac.jp
Nan LuUniversity of TübingenAdresse e-mail validée de uni-tuebingen.de
Nannan WangProfessor, Xidian UniversityAdresse e-mail validée de xidian.edu.cn
Bo DaiGoogle Brain & Georgia TechAdresse e-mail validée de google.com

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Gang Niu

Autres noms牛罡

RIKEN Center for Advanced Intelligence Project

Adresse e-mail validée de riken.jp - Page d'accueil

Machine Learning


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
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	1999	2018
How does disagreement help generalization against label corruption? X Yu, B Han, J Yao, G Niu, IW Tsang, M Sugiyama ICML 2019, 2019	768	2019
Positive-unlabeled learning with non-negative risk estimator R Kiryo, G Niu, MC Plessis, M Sugiyama NeurIPS 2017 (oral), 2017	494	2017
Analysis of learning from positive and unlabeled data MC du Plessis, G Niu, M Sugiyama NeurIPS 2014, 2014	415	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 ICML 2020, 2020	393	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	351	2019
Convex formulation for learning from positive and unlabeled data MC du Plessis, G Niu, M Sugiyama ICML 2015, 2015	348	2015
Does distributionally robust supervised learning give robust classifiers? W Hu, G Niu, I Sato, M Sugiyama ICML 2018, 2018	288	2018
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	255	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	249	2018
Class-prior estimation for learning from positive and unlabeled data MC du Plessis, G Niu, M Sugiyama Machine Learning 106 (4), 463--492, 2017	249*	2017
Geometry-aware instance-reweighted adversarial training J Zhang, J Zhu, G Niu, B Han, M Sugiyama, M Kankanhalli ICLR 2021 (oral), 2021	248	2021
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	208	2020
Learning with noisy labels revisited: A study using real-world human annotations J Wei, Z Zhu, H Cheng, T Liu, G Niu, Y Liu ICLR 2022, 2022	183	2022
Analysis and improvement of policy gradient estimation T Zhao, H Hachiya, G Niu, M Sugiyama NeurIPS 2011, 2011	173	2011
Learning from complementary labels T Ishida, G Niu, W Hu, M Sugiyama NeurIPS 2017, 2017	165	2017
Progressive identification of true labels for partial-label learning J Lv, M Xu, L Feng, G Niu, X Geng, M Sugiyama ICML 2020, 2020	161	2020
Understanding and improving early stopping for learning with noisy labels Y Bai, E Yang, B Han, Y Yang, J Li, Y Mao, G Niu, T Liu NeurIPS 2021, 2021	154	2021
A Survey of Label-noise Representation Learning: Past, Present and Future B Han, Q Yao, T Liu, G Niu, IW Tsang, JT Kwok, M Sugiyama arXiv preprint arXiv:2011.04406, 2020	138	2020
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	137*	2020

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