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Gang Niu
Gang Niu
RIKEN Center for Advanced Intelligence Project
Bestätigte E-Mail-Adresse bei riken.jp - Startseite
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Zitiert von
Zitiert von
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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
20412018
How does disagreement help generalization against label corruption?
X Yu, B Han, J Yao, G Niu, IW Tsang, M Sugiyama
ICML 2019, 2019
7802019
Positive-unlabeled learning with non-negative risk estimator
R Kiryo, G Niu, MC Plessis, M Sugiyama
NeurIPS 2017 (oral), 2017
4992017
Analysis of learning from positive and unlabeled data
MC du Plessis, G Niu, M Sugiyama
NeurIPS 2014, 2014
4172014
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
4012020
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
3612019
Convex formulation for learning from positive and unlabeled data
MC du Plessis, G Niu, M Sugiyama
ICML 2015, 2015
3502015
Does distributionally robust supervised learning give robust classifiers?
W Hu, G Niu, I Sato, M Sugiyama
ICML 2018, 2018
2972018
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
2622020
Geometry-aware instance-reweighted adversarial training
J Zhang, J Zhu, G Niu, B Han, M Sugiyama, M Kankanhalli
ICLR 2021 (oral), 2021
2542021
Class-prior estimation for learning from positive and unlabeled data
MC du Plessis, G Niu, M Sugiyama
Machine Learning 106 (4), 463--492, 2017
254*2017
Masking: A new perspective of noisy supervision
B Han, J Yao, G Niu, M Zhou, IW Tsang, Y Zhang, M Sugiyama
NeurIPS 2018, 2018
2522018
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
2122020
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
1932022
Learning from complementary labels
T Ishida, G Niu, W Hu, M Sugiyama
NeurIPS 2017, 2017
1662017
Progressive identification of true labels for partial-label learning
J Lv, M Xu, L Feng, G Niu, X Geng, M Sugiyama
ICML 2020, 2020
1642020
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
1622021
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
1442020
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
140*2020
Do we need zero training loss after achieving zero training error?
T Ishida, I Yamane, T Sakai, G Niu, M Sugiyama
ICML 2020, 2020
1372020
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