Bo Han

引用次数

	总计	2019 年至今
引用	10747	10638
h 指数	43	43
i10 指数	115	113

3900

1950

975

2925

201820192020202120222023202433 151 504 1267 1902 2768 3898

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

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Bo Han

HKBU / RIKEN

在 comp.hkbu.edu.hk 的电子邮件经过验证 - 首页

Machine Learning Deep Learning Artificial Intelligence Trustworthy Machine Learning


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
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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