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
Nannan WangProfessor, Xidian UniversityVerified email at xidian.edu.cn
Mingming GongUniversity of MelbourneVerified email at unimelb.edu.au
Jiangchao YaoShanghai Jiao Tong UniversityVerified email at sjtu.edu.cn
Miao XuUniversity of Queensland, RIKEN AIPVerified email at uq.edu.au
Chen GongProfessor@Nanjing University of Science and TechnologyVerified email at njust.edu.cn
Jingfeng ZhangPostdoctoral researcher @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) -- 刘峰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
Lei FengChongqing University / RIKEN Center for Advanced Intelligence ProjectVerified email at cqu.edu.cn
James T. KwokProfessor of Computer Science and Engineering, Hong Kong University of Science and TechnologyVerified email at cse.ust.hk
Mingyuan ZhouAssociate Professor, University of Texas at AustinVerified email at mccombs.utexas.edu
Hongxia YangAlibaba Group, Yahoo!, IBM WatsonVerified email at alibaba-inc.com

Bo Han

HKBU / RIKEN

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

Machine Learning Data Mining 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	1275	2018
How does Disagreement Help Generalization against Label Corruption? X Yu, B Han, J Yao, G Niu, IW Tsang, M Sugiyama ICML 2019, 2019	468	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	218	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	194	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	187	2018
Geometry-aware Instance-reweighted Adversarial Training J Zhang, J Zhu, G Niu, B Han, M Sugiyama, M Kankanhalli ICLR 2021, 2021	144	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	117	2020
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	106*	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	101	2021
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	93*	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	70*	2019
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	67	2020
Confidence Scores Make Instance-dependent Label-noise Learning Possible A Berthon, B Han, G Niu, T Liu, M Sugiyama ICML 2021, 2021	53	2021
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	53	2020
Learning with Multiple Complementary Labels L Feng, T Kaneko, B Han, G Niu, B An, M Sugiyama arXiv preprint arXiv:1912.12927, 2019	50	2019
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	46*	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	42	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	41*	2020
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	40*	2021
Towards Robust ResNet: A Small Step but A Giant Leap J Zhang, B Han, L Wynter, KH Low, M Kankanhalli IJCAI 2019, 2019	30	2019

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