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 NiuRIKEN 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
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
Jiangchao YaoShanghai Jiao Tong UniversityVerified email at sjtu.edu.cn
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 XiaThe University of SydneyVerified email at uni.sydney.edu.au
Mingming GongUniversity of Melbourne & Mohamed bin Zayed University of Artificial IntelligenceVerified 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
Feng Liu (he/him)Lecturer, University of Melbourne / Visiting Scientist, RIKEN-AIP / Visiting Fellow, UTS-AAIIVerified email at unimelb.edu.au
Yonggang ZHANGHKBUVerified email at comp.hkbu.edu.hk
Miao XuUniversity of Queensland, RIKEN AIPVerified email at uq.edu.au
Xingrui YuPh.D Student, University of Technology Sydney (UTS)Verified email at student.uts.edu.au
Yongqiang ChenThe Chinese University of Hong KongVerified email at cse.cuhk.edu.hk
Jianing ZhuPh.D. student, HKBUVerified email at comp.hkbu.edu.hk
Lei FengSingapore University of Technology and DesignVerified email at sutd.edu.sg
Qizhou WangHong Kong Baptist UniversityVerified email at comp.hkbu.edu.hk
Zhanke ZhouPh.D. student, HKBUVerified email at comp.hkbu.edu.hk

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	2268	2018
How does Disagreement Help Generalization against Label Corruption? X Yu, B Han, J Yao, G Niu, IW Tsang, M Sugiyama ICML 2019, 2019	870	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	442	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	390	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	293	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	290	2021
Geometry-aware Instance-reweighted Adversarial Training J Zhang, J Zhu, G Niu, B Han, M Sugiyama, M Kankanhalli ICLR 2021, 2021	288	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	276	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	241*	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	198*	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	165	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	155	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	149*	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	137*	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	135*	2019
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	132	2022
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	129	2021
Is Out-of-distribution Detection Learnable? Z Fang, S Li, J Lu, J Dong, B Han, F Liu NeurIPS 2022, 2022	117	2022
Confidence Scores Make Instance-dependent Label-noise Learning Possible A Berthon, B Han, G Niu, T Liu, M Sugiyama ICML 2021, 2021	115	2021
Learning with Multiple Complementary Labels L Feng, T Kaneko, B Han, G Niu, B An, M Sugiyama arXiv preprint arXiv:1912.12927, 2019	102	2019

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