Bo Han

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Tongliang LiuDirector, Sydney AI Centre, University of Sydney & Mohamed bin Zayed University of AIVerified email at sydney.edu.au
Gang NiuSenior Research Scientist, RIKEN Center for Advanced Intelligence ProjectVerified email at riken.jp
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
Jiangchao YaoShanghai Jiao Tong UniversityVerified email at sjtu.edu.cn
Ivor W. Tsang, IEEE FellowInaugural A*STAR AI Fellow and Director, Centre for Frontier AI Research, A*STARVerified email at cfar.a-star.edu.sg
Quanming YaoAssistant Professor, EE Department, Tsinghua UniversityVerified email at tsinghua.edu.cn
Chen GongProfessor@Nanjing University of Science and TechnologyVerified email at njust.edu.cn
Xiaobo XiaPostdoc, National University of SingaporeVerified email at nus.edu.sg
Mingming GongUniversity of Melbourne & Mohamed bin Zayed University of Artificial IntelligenceVerified email at unimelb.edu.au
Feng Liu (he/him)Lecturer, University of Melbourne / Visiting Scientist, RIKEN-AIP / Visiting Fellow, UTS-AAIIVerified email at unimelb.edu.au
Nannan WangProfessor, Xidian UniversityVerified email at xidian.edu.cn
Jingfeng ZhangLecturer@University of Auckland & Scientist@RIKEN AIPVerified email at riken.jp
Yonggang ZHANGHKBUVerified email at comp.hkbu.edu.hk
Miao XuUniversity of Queensland, RIKEN AIPVerified email at uq.edu.au
Xingrui YuScientist, Centre for Frontier AI Research (CFAR), A*STARVerified email at cfar.a-star.edu.sg
Jianing ZhuPh.D. student, HKBUVerified email at comp.hkbu.edu.hk
Yongqiang ChenThe Chinese University of Hong KongVerified email at cse.cuhk.edu.hk
Zhanke ZhouPh.D. student, HKBUVerified email at comp.hkbu.edu.hk
Qizhou WangPh.D. Student, HKBUVerified email at comp.hkbu.edu.hk
Lei FengSingapore University of Technology and DesignVerified email at sutd.edu.sg

Bo Han

HKBU / RIKEN

Verified email at comp.hkbu.edu.hk - Homepage

Machine Learning Deep Learning Artificial Intelligence Trustworthy Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	2376	2018
How does Disagreement Help Generalization against Label Corruption? X Yu, B Han, J Yao, G Niu, IW Tsang, M Sugiyama ICML 2019, 2019	909	2019
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	470	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	408	2019
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, 2020	323	2020
Robust Early-learning: Hindering the Memorization of Noisy Labels X Xia, T Liu, B Han, C Gong, N Wang, Z Ge, Y Chang ICLR 2021, 2021	310	2021
Geometry-aware Instance-reweighted Adversarial Training J Zhang, J Zhu, G Niu, B Han, M Sugiyama, M Kankanhalli ICLR 2021, 2021	309	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	286	2018
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	258*	2020
Understanding and Improving for Learning with Noisy Labels Y Bai, E Yang, B Han, Y Yang, J Li, Y Mao, G Niu, T Liu arXiv preprint arXiv:2106.15853, 2021	225*	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	184	2020
Provably Consistent Partial-Label Learning L Feng, J Lv, B Han, M Xu, G Niu, X Geng, B An, M Sugiyama arXiv preprint arXiv:2007.08929, 2020	165	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	158*	2020
Learning Causally Invariant Representations for Out-of-distribution Generalization on Graphs Y Chen, Y Zhang, Y Bian, H Yang, K Ma, B Xie, T Liu, B Han, J Cheng arXiv preprint arXiv:2202.05441, 2022	154*	2022
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels X Xia, T Liu, B Han, M Gong, J Yu, G Niu, M Sugiyama arXiv preprint arXiv:2106.00445, 2022	143	2022
Searching to Exploit Memorization Effect in Learning with Noisy Labels Q Yao, H Yang, B Han, G Niu, JT Kwok arXiv preprint arXiv:1911.02377, 2019	141*	2019
Provably End-to-end Label-Noise Learning without Anchor Points X Li, T Liu, B Han, G Niu, M Sugiyama arXiv preprint arXiv:2102.02400, 2021	138	2021
Is Out-of-distribution Detection Learnable? Z Fang, S Li, J Lu, J Dong, B Han, F Liu NeurIPS 2022, 2022	127	2022
Confidence Scores Make Instance-dependent Label-noise Learning Possible A Berthon, B Han, G Niu, T Liu, M Sugiyama ICML 2021, 2021	119	2021
Learning with Multiple Complementary Labels L Feng, T Kaneko, B Han, G Niu, B An, M Sugiyama arXiv preprint arXiv:1912.12927, 2019	108	2019

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