Gang Niu

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Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoEmail confirmado em k.u-tokyo.ac.jp
Bo HanHKBU / RIKENEmail confirmado em comp.hkbu.edu.hk
Tongliang LiuDirector, Sydney AI Centre, University of Sydney & Mohamed bin Zayed University of AIEmail confirmado em sydney.edu.au
Miao XuUniversity of QueenslandEmail confirmado em uq.edu.au
Ivor W. Tsang, IEEE FellowInaugural A*STAR AI Fellow and Director, Centre for Frontier AI Research, A*STAREmail confirmado em cfar.a-star.edu.sg
Lei FengSingapore University of Technology and DesignEmail confirmado em sutd.edu.sg
Jingfeng ZhangLecturer@University of Auckland & Scientist@RIKEN AIPEmail confirmado em riken.jp
Quanming YaoAssistant Professor, EE Department, Tsinghua UniversityEmail confirmado em tsinghua.edu.cn
Mingming GongUniversity of Melbourne & Mohamed bin Zayed University of Artificial IntelligenceEmail confirmado em unimelb.edu.au
Xingrui YuScientist, Centre for Frontier AI Research (CFAR), A*STAREmail confirmado em cfar.a-star.edu.sg
Weihua HuStanfordEmail confirmado em stanford.edu
Chen GongProfessorEmail confirmado em sjtu.edu.cn
Jiangchao YaoShanghai Jiao Tong UniversityEmail confirmado em sjtu.edu.cn
Bo AnNanyang Technological UniversityEmail confirmado em ntu.edu.sg
Tomoya SakaiIBM Research - TokyoEmail confirmado em ibm.com
Xiaobo XiaPostdoc, National University of SingaporeEmail confirmado em nus.edu.sg
Takashi IshidaResearch Scientist, RIKEN AIP / Lecturer, University of TokyoEmail confirmado em ms.k.u-tokyo.ac.jp
Nan LuUniversity of TübingenEmail confirmado em uni-tuebingen.de
Nannan WangProfessor, Xidian UniversityEmail confirmado em xidian.edu.cn
Yang LiuComputer Science and Engineering, UC Santa CruzEmail confirmado em ucsc.edu

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Gang Niu

Outros nomes牛罡

Senior Research Scientist (tenured), RIKEN Center for Advanced Intelligence Project

Email confirmado em riken.jp - Página inicial

Machine Learning


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
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	2493	2018
How does disagreement help generalization against label corruption? X Yu, B Han, J Yao, G Niu, IW Tsang, M Sugiyama ICML 2019, 2019	949	2019
Positive-unlabeled learning with non-negative risk estimator R Kiryo, G Niu, MC Plessis, M Sugiyama NeurIPS 2017 (oral), 2017	579	2017
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	483	2020
Analysis of learning from positive and unlabeled data MC du Plessis, G Niu, M Sugiyama NeurIPS 2014, 2014	471	2014
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	417	2019
Convex formulation for learning from positive and unlabeled data MC du Plessis, G Niu, M Sugiyama ICML 2015, 2015	397	2015
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	335	2020
Does distributionally robust supervised learning give robust classifiers? W Hu, G Niu, I Sato, M Sugiyama ICML 2018, 2018	335	2018
Geometry-aware instance-reweighted adversarial training J Zhang, J Zhu, G Niu, B Han, M Sugiyama, M Kankanhalli ICLR 2021 (oral), 2021	322	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	296	2018
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	284	2022
Class-prior estimation for learning from positive and unlabeled data MC du Plessis, G Niu, M Sugiyama Machine Learning 106 (4), 463--492, 2017	273*	2017
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	266	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	246	2021
Progressive identification of true labels for partial-label learning J Lv, M Xu, L Feng, G Niu, X Geng, M Sugiyama ICML 2020, 2020	208	2020
Learning from complementary labels T Ishida, G Niu, W Hu, M Sugiyama NeurIPS 2017, 2017	196	2017
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	195	2020
Analysis and improvement of policy gradient estimation T Zhao, H Hachiya, G Niu, M Sugiyama NeurIPS 2011, 2011	191	2011
PiCO: Contrastive label disambiguation for partial label learning H Wang, R Xiao, Y Li, L Feng, G Niu, G Chen, J Zhao ICLR 2022 (outstanding paper honorable mention), 2022	173	2022

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