Bo Han

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Gang NiuRIKEN Center for Advanced Intelligence ProjectVerified email at postman.riken.jp
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
Tongliang LiuDirector, Sydney AI Centre, University of Sydney & Mohamed bin Zayed University of AIVerified email at sydney.edu.au
Ivor W. Tsang, IEEE FellowInaugural A*STAR AI Fellow and Director, Centre for Frontier AI Research, A*STARVerified email at ihpc.a-star.edu.sg
Quanming YaoAssistant Professor, EE Department, Tsinghua UniversityVerified email at tsinghua.edu.cn
Jiangchao YaoShanghai Jiao Tong UniversityVerified email at sjtu.edu.cn
Nannan WangProfessor, Xidian UniversityVerified email at xidian.edu.cn
Chen GongProfessor@Nanjing University of Science and TechnologyVerified email at njust.edu.cn
Miao XuUniversity of Queensland, RIKEN AIPVerified email at uq.edu.au
Mingming GongUniversity of MelbourneVerified email at unimelb.edu.au
Jingfeng ZhangResearch Scientist @RIKEN AIPVerified email at riken.jp
Xiaobo XiaPh.D. student, The University of SydneyVerified email at uni.sydney.edu.au
Xingrui YuPh.D Student, University of Technology Sydney (UTS)Verified email at student.uts.edu.au
Feng Liu (he/him)Lecturer in Statistics (Data Science), The University of MelbourneVerified email at ieee.org
Weihua HuKumo.aiVerified email at kumo.ai
Mohan KankanhalliProfessor of Computer Science, National University of SingaporeVerified email at comp.nus.edu.sg
Jianing ZhuPh.D. student, HKBUVerified email at comp.hkbu.edu.hk
James T. KwokProfessor of Computer Science and Engineering, Hong Kong University of Science and TechnologyVerified email at cse.ust.hk
Yonggang ZHANGHKBUVerified email at comp.hkbu.edu.hk
Lei FengNanyang Technological University / RIKEN Center for Advanced Intelligence ProjectVerified email at e.ntu.edu.sg

Bo Han

HKBU / RIKEN

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

Machine Learning Deep Learning Artificial Intelligence


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	1434	2018
How does Disagreement Help Generalization against Label Corruption? X Yu, B Han, J Yao, G Niu, IW Tsang, M Sugiyama ICML 2019, 2019	533	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	262	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	230	2019
Masking: A New Perspective of Noisy Supervision B Han, J Yao, G Niu, M Zhou, IW Tsang, Y Zhang, M Sugiyama NeurIPS 2018, 2018	203	2018
Geometry-aware Instance-reweighted Adversarial Training J Zhang, J Zhu, G Niu, B Han, M Sugiyama, M Kankanhalli ICLR 2021, 2021	168	2021
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	147	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	129	2021
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	129*	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	106*	2020
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	82	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	79*	2019
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	73	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	66*	2021
Learning with Multiple Complementary Labels L Feng, T Kaneko, B Han, G Niu, B An, M Sugiyama arXiv preprint arXiv:1912.12927, 2019	64	2019
Confidence Scores Make Instance-dependent Label-noise Learning Possible A Berthon, B Han, G Niu, T Liu, M Sugiyama ICML 2021, 2021	61	2021
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	54	2021
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks R Gao, F Liu, J Zhang, B Han, T Liu, G Niu, M Sugiyama ICML 2021, 2021	45*	2021
Class2simi: A Noise Reduction Perspective on Learning with Noisy Labels S Wu, X Xia, T Liu, B Han, M Gong, N Wang, H Liu, G Niu arXiv preprint arXiv:2006.07831, 2020	45*	2020
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	38	2022

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