| Co-teaching: Robust training of deep neural networks with extremely noisy labels B Han, Q Yao, X Yu, G Niu, M Xu, W Hu, I Tsang, M Sugiyama NeurIPS 2018, 2018 | 2992 | 2018 |
| How does disagreement help generalization against label corruption? X Yu, B Han, J Yao, G Niu, IW Tsang, M Sugiyama ICML 2019, 2019 | 1150 | 2019 |
| Positive-unlabeled learning with non-negative risk estimator R Kiryo, G Niu, MC Plessis, M Sugiyama NeurIPS 2017 (oral), 2017 | 709 | 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 | 561 | 2020 |
| Analysis of learning from positive and unlabeled data MC du Plessis, G Niu, M Sugiyama NeurIPS 2014, 2014 | 535 | 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 | 508 | 2019 |
| Convex formulation for learning from positive and unlabeled data MC du Plessis, G Niu, M Sugiyama ICML 2015, 2015 | 457 | 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 | 402 | 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 | 401 | 2022 |
| Geometry-aware instance-reweighted adversarial training J Zhang, J Zhu, G Niu, B Han, M Sugiyama, M Kankanhalli ICLR 2021 (oral), 2021 | 378 | 2021 |
| Does distributionally robust supervised learning give robust classifiers? W Hu, G Niu, I Sato, M Sugiyama ICML 2018, 2018 | 377 | 2018 |
| 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 | 358 | 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 | 333 | 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 | 322 | 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 | 298* | 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 | 264 | 2020 |
| Learning from complementary labels T Ishida, G Niu, W Hu, M Sugiyama NeurIPS 2017, 2017 | 239 | 2017 |
| 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 | 238* | 2022 |
| 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 | 230 | 2020 |
| Provably consistent partial-label learning L Feng, J Lv, B Han, M Xu, G Niu, X Geng, B An, M Sugiyama NeurIPS 2020, 2020 | 219 | 2020 |
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
