Bo Han

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Tongliang LiuDirector, Sydney AI Centre, University of Sydney & Mohamed bin Zayed University of AIEmail confirmado em sydney.edu.au
Gang NiuSenior Research Scientist (tenured), RIKEN Center for Advanced Intelligence ProjectEmail confirmado em riken.jp
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoEmail confirmado em k.u-tokyo.ac.jp
Jiangchao YaoShanghai Jiao Tong UniversityEmail confirmado em sjtu.edu.cn
Ivor W. Tsang, IEEE FellowInaugural A*STAR AI Fellow and Director, Centre for Frontier AI Research, A*STAREmail confirmado em cfar.a-star.edu.sg
Quanming YaoAssistant Professor, EE Department, Tsinghua UniversityEmail confirmado em tsinghua.edu.cn
Chen GongProfessorEmail confirmado em sjtu.edu.cn
Xiaobo XiaPostdoc, National University of SingaporeEmail confirmado em nus.edu.sg
Mingming GongUniversity of Melbourne & Mohamed bin Zayed University of Artificial IntelligenceEmail confirmado em unimelb.edu.au
Feng Liu (he/him)Lecturer, University of Melbourne / Visiting Scientist, RIKEN-AIP / Visiting Fellow, UTS-AAIIEmail confirmado em unimelb.edu.au
Yonggang ZHANGHKBUEmail confirmado em comp.hkbu.edu.hk
Nannan WangProfessor, Xidian UniversityEmail confirmado em xidian.edu.cn
Jingfeng ZhangLecturer@University of Auckland & Scientist@RIKEN AIPEmail confirmado em riken.jp
Miao XuUniversity of QueenslandEmail confirmado em uq.edu.au
Xingrui YuScientist, CFAR, A*STAREmail confirmado em cfar.a-star.edu.sg
Jianing ZhuPh.D. student, HKBUEmail confirmado em comp.hkbu.edu.hk
Yongqiang ChenThe Chinese University of Hong KongEmail confirmado em cse.cuhk.edu.hk
Zhanke ZhouHKBU / StanfordEmail confirmado em comp.hkbu.edu.hk
Qizhou WangPh.D. Student, HKBUEmail confirmado em comp.hkbu.edu.hk
Lei FengProfessor, Southeast UniversityEmail confirmado em seu.edu.cn

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Bo Han

HKBU / RIKEN

Email confirmado em comp.hkbu.edu.hk - Página inicial

Machine Learning Deep Learning Artificial Intelligence Trustworthy Machine Learning


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
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	2528	2018
How does Disagreement Help Generalization against Label Corruption? X Yu, B Han, J Yao, G Niu, IW Tsang, M Sugiyama ICML 2019, 2019	967	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	487	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	437	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	343	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	334	2021
Geometry-aware Instance-reweighted Adversarial Training J Zhang, J Zhu, G Niu, B Han, M Sugiyama, M Kankanhalli ICLR 2021, 2021	322	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	297	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	277*	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	257*	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	199	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	190*	2022
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	180	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	169*	2020
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	157*	2019
Deepinception: Hypnotize large language model to be jailbreaker X Li, Z Zhou, J Zhu, J Yao, T Liu, B Han arXiv preprint arXiv:2311.03191, 2023	156	2023
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	153	2021
Is Out-of-distribution Detection Learnable? Z Fang, S Li, J Lu, J Dong, B Han, F Liu NeurIPS 2022, 2022	151	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	151	2022
Confidence Scores Make Instance-dependent Label-noise Learning Possible A Berthon, B Han, G Niu, T Liu, M Sugiyama ICML 2021, 2021	127	2021

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