Bo Han

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Gang NiuRIKEN Center for Advanced Intelligence ProjectAdresse e-mail validée de postman.riken.jp
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoAdresse e-mail validée de k.u-tokyo.ac.jp
Ivor W. TsangResearch Director & Professor, Australian AI Institute, University of Technology SydneyAdresse e-mail validée de uts.edu.au
Tongliang LiuUniversity of Sydney and Visiting Scientist at RIKEN AIPAdresse e-mail validée de sydney.edu.au
Quanming YaoAssistant Professor, Tsinghua UniversityAdresse e-mail validée de tsinghua.edu.cn
Miao XuRIKEN, AIPAdresse e-mail validée de lamda.nju.edu.cn
Xingrui YuPh.D Student, University of Technology Sydney (UTS)Adresse e-mail validée de student.uts.edu.au
Jiangchao YaoAlibaba GroupAdresse e-mail validée de sjtu.edu.cn
Xiaobo XiaPh.D student, The University of SydneyAdresse e-mail validée de uni.sydney.edu.au
Nannan WangProfessor, Xidian UniversityAdresse e-mail validée de xidian.edu.cn
Mingming GongThe University of MelbourneAdresse e-mail validée de unimelb.edu.au
Chen GongProfessor@Nanjing University of Science and TechnologyAdresse e-mail validée de sjtu.edu.cn
Weihua HuPh.D. student, Stanford UniversityAdresse e-mail validée de stanford.edu
Jingfeng ZhangPh.D., NUS, Singapore -> Postdoc, RIKEN-AIP, TokyoAdresse e-mail validée de riken.jp
Mohan KankanhalliProfessor of Computer Science, National University of SingaporeAdresse e-mail validée de comp.nus.edu.sg
Ling Chenuniversity of technology sydneyAdresse e-mail validée de uts.edu.au
James T. KwokProfessor of Computer Science and Engineering, Hong Kong University of Science and TechnologyAdresse e-mail validée de cse.ust.hk
Lei FengChongqing University, ChinaAdresse e-mail validée de cqu.edu.cn
Mingyuan ZhouAssociate Professor, University of Texas at AustinAdresse e-mail validée de mccombs.utexas.edu
Mohammad Emtiyaz KhanCenter for Advanced Intelligence Project (AIP), RIKEN, TokyoAdresse e-mail validée de postman.riken.jp

Bo Han

HKBU / RIKEN

Adresse e-mail validée de comp.hkbu.edu.hk - Page d'accueil

Machine Learning


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
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	468	2018
How does Disagreement Help Generalization against Label Corruption? X Yu, B Han, J Yao, G Niu, IW Tsang, M Sugiyama ICML 2019, 2019	152*	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	99	2018
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	74	2019
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	37*	2020
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	34	2020
Progressive Stochastic Learning for Noisy Labels B Han, IW Tsang, L Chen, C Yu, SF Fung IEEE Transactions on Neural Networks and Learning Systems, 2017	24	2017
Dual T: 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	23	2020
Searching to Exploit Memorization Effect in Learning with Noisy Labels Q Yao, H Yang, B Han, G Niu, JT Kwok ICML 2020, 2020	23*	2020
On the Convergence of a Family of Robust Losses for Stochastic Gradient Descent B Han, IW Tsang, L Chen ECML 2016, 2016	20	2016
Learning from Multiple Complementary Labels L Feng, T Kaneko, B Han, G Niu, B An, M Sugiyama ICML 2020, 2020	19	2020
Towards Robust ResNet: A Small Step but A Giant Leap J Zhang, B Han, L Wynter, KH Low, M Kankanhalli IJCAI 2019, 2019	19	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	18	2020
Geometry-aware Instance-reweighted Adversarial Training J Zhang, J Zhu, G Niu, B Han, M Sugiyama, M Kankanhalli ICLR 2021, 2021	17	2021
Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations Q Yao, JT Kwok, B Han ICML 2019, 2019	17*	2019
Fast Image Recognition based on Independent Component Analysis S Zhang, B He, R Nian, J Wang, B Han, A Lendasse, G Yuan Cognitive Computation, 2014	17	2014
LARSEN: Selective Ensemble Learning using LARS for Blended Data B Han, B He, R Nian, M Ma, S Zhang, M Li, A Lendasse Neurocomputing, 2015	13*	2015
Confidence Scores Make Instance-dependent Label-noise Learning Possible A Berthon, B Han, G Niu, T Liu, M Sugiyama ICML 2021, 2021	12	2021
Butterfly: One-step Approach towards Wildly Unsupervised Domain Adaptation F Liu, J Lu, B Han, G Niu, G Zhang, M Sugiyama arXiv preprint arXiv:1905.07720, 2019	11*	2019
Robust Plackett–Luce Model for k-ary Crowdsourced Preferences B Han, Y Pan, IW Tsang Machine Learning Journal, 2017	11	2017

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