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
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
Tongliang LiuUniversity of Sydney and Visiting Scientist at RIKEN AIPVerified email at sydney.edu.au
Quanming YaoSenior Scientist, 4ParadigmVerified email at cse.ust.hk
Tomoya SakaiNEC CorporationVerified email at nec.com
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
Nannan WangProfessor, Xidian UniversityVerified email at xidian.edu.cn
Takashi IshidaPh.D. student, The University of TokyoVerified email at ms.k.u-tokyo.ac.jp
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
Shang LinDept. of Computer Science and Technology, Nanjing UniversityVerified email at nju.edu.cn
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


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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	401	2018
Analysis of learning from positive and unlabeled data MC du Plessis, G Niu, M Sugiyama NeurIPS 2014, 2014	188	2014
Positive-unlabeled learning with non-negative risk estimator R Kiryo, G Niu, MC Plessis, M Sugiyama NeurIPS 2017 (oral), 2017	182	2017
Convex formulation for learning from positive and unlabeled data MC du Plessis, G Niu, M Sugiyama ICML 2015, 2015	163	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	142*	2017
How does disagreement help generalization against label corruption? X Yu, B Han, J Yao, G Niu, IW Tsang, M Sugiyama ICML 2019, 2019	126*	2019
Analysis and improvement of policy gradient estimation T Zhao, H Hachiya, G Niu, M Sugiyama NeurIPS 2011, 2011	114	2011
Masking: A new perspective of noisy supervision B Han, J Yao, G Niu, M Zhou, IW Tsang, Y Zhang, M Sugiyama NeurIPS 2018, 2018	86	2018
Information-theoretic semi-supervised metric learning via entropy regularization G Niu, B Dai, M Yamada, M Sugiyama ICML 2012, 2012	82	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	77	2016
Semi-supervised classification based on classification from positive and unlabeled data T Sakai, MC du Plessis, G Niu, M Sugiyama ICML 2017, 2017	74	2017
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	73*	2014
Does distributionally robust supervised learning give robust classifiers? W Hu, G Niu, I Sato, M Sugiyama ICML 2018, 2018	63	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	59	2019
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	52	2013
Learning from complementary labels T Ishida, G Niu, W Hu, M Sugiyama NeurIPS 2017, 2017	47	2017
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
Classification from pairwise similarity and unlabeled data H Bao, G Niu, M Sugiyama ICML 2018, 2018	38	2018
Semi-supervised AUC optimization based on positive-unlabeled learning T Sakai, G Niu, M Sugiyama Machine Learning 107 (4), 767--794, 2018	32	2018
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	29*	2020

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