Miao Xu

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	Total	Desde 2019
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Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoDirección de correo verificada de k.u-tokyo.ac.jp
Gang NiuSenior Research Scientist, RIKEN Center for Advanced Intelligence ProjectDirección de correo verificada de riken.jp
Bo HanHKBU / RIKENDirección de correo verificada de comp.hkbu.edu.hk
Ivor W. Tsang, IEEE FellowInaugural A*STAR AI Fellow and Director, Centre for Frontier AI Research, A*STARDirección de correo verificada de cfar.a-star.edu.sg
Quanming YaoAssistant Professor, EE Department, Tsinghua UniversityDirección de correo verificada de tsinghua.edu.cn
Weitong ChenThe University of AdelaideDirección de correo verificada de adelaide.edu.au
Zhi-Hua ZhouNanjing UniversityDirección de correo verificada de nju.edu.cn
Weihua HuStanfordDirección de correo verificada de stanford.edu
Jiaqi LvSoutheast UniversityDirección de correo verificada de seu.edu.cn
Rong JinAlibaba GroupDirección de correo verificada de cse.msu.edu
Yu-Feng LiProfessor, Nanjing UniversityDirección de correo verificada de lamda.nju.edu.cn
Sheng-Jun HuangNanjing University of Aeronautics & AstronauticsDirección de correo verificada de lamda.nju.edu.cn
Songcan ChenNanjing University of Aeronautics & AstronauticsDirección de correo verificada de nuaa.edu.cn
Hsuan-Tien LinProfessor of Computer Science and Information Engineering, National Taiwan UniversityDirección de correo verificada de csie.ntu.edu.tw
Takeshi TeshimaRecruit Co., Ltd.Dirección de correo verificada de a.riken.jp

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Miao Xu

University of Queensland, RIKEN AIP

Dirección de correo verificada de uq.edu.au - Página principal

Machine Learning Data Mining


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Co-teaching: Robust training of deep neural networks with extremely noisy labels B Han, Q Yao, X Yu, G Niu, M Xu, W Hu, I Tsang, M Sugiyama Advances in neural information processing systems 31, 2018	2347	2018
Speedup matrix completion with side information: Application to multi-label learning M Xu, R Jin, ZH Zhou Advances in neural information processing systems 26, 2013	341	2013
Progressive identification of true labels for partial-label learning J Lv, M Xu, L Feng, G Niu, X Geng, M Sugiyama international conference on machine learning, 6500-6510, 2020	197	2020
Provably consistent partial-label learning L Feng, J Lv, B Han, M Xu, G Niu, X Geng, B An, M Sugiyama Advances in neural information processing systems 33, 10948-10960, 2020	164	2020
Sigua: Forgetting may make learning with noisy labels more robust B Han, G Niu, X Yu, Q Yao, M Xu, I Tsang, M Sugiyama International Conference on Machine Learning, 4006-4016, 2020	137	2020
Positive-Unlabeled Learning from Imbalanced Data. G Su, W Chen, M Xu IJCAI, 2995-3001, 2021	67	2021
Incomplete Label Distribution Learning. M Xu, ZH Zhou IJCAI, 3175-3181, 2017	59	2017
Active feature acquisition with supervised matrix completion SJ Huang, M Xu, MK Xie, M Sugiyama, G Niu, S Chen Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018	57	2018
CUR algorithm for partially observed matrices M Xu, R Jin, ZH Zhou International Conference on Machine Learning, 1412-1421, 2015	44	2015
Self-Supervised Adversarial Distribution Regularization for Medication Recommendation. Y Wang, W Chen, D Pi, L Yue, S Wang, M Xu IJCAI, 3134-3140, 2021	36	2021
Multi-label learning with PRO loss M Xu, YF Li, ZH Zhou Proceedings of the AAAI Conference on Artificial Intelligence 27 (1), 998-1004, 2013	34	2013
On the robustness of average losses for partial-label learning J Lv, B Liu, L Feng, N Xu, M Xu, B An, G Niu, X Geng, M Sugiyama IEEE Transactions on Pattern Analysis and Machine Intelligence 46 (5), 2569-2583, 2023	30	2023
Robust multi-label learning with PRO loss M Xu, YF Li, ZH Zhou IEEE Transactions on Knowledge and Data Engineering 32 (8), 1610-1624, 2019	27	2019
Fair representation learning: An alternative to mutual information J Liu, Z Li, Y Yao, F Xu, X Ma, M Xu, H Tong Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022	25	2022
Learning from group supervision: the impact of supervision deficiency on multi-label learning M Xu, LZ Guo Science China Information Sciences 64, 1-13, 2021	25	2021
Pointwise binary classification with pairwise confidence comparisons L Feng, S Shu, N Lu, B Han, M Xu, G Niu, B An, M Sugiyama International Conference on Machine Learning, 3252-3262, 2021	23	2021
Pumpout: A meta approach for robustly training deep neural networks with noisy labels B Han, G Niu, J Yao, X Yu, M Xu, I Tsang, M Sugiyama	19	2018
Pre-training in medical data: A survey Y Qiu, F Lin, W Chen, M Xu Machine Intelligence Research 20 (2), 147-179, 2023	18	2023
Matrix co-completion for multi-label classification with missing features and labels M Xu, G Niu, B Han, IW Tsang, ZH Zhou, M Sugiyama arXiv preprint arXiv:1805.09156, 2018	17	2018
Revisiting sample selection approach to positive-unlabeled learning: Turning unlabeled data into positive rather than negative M Xu, B Li, G Niu, B Han, M Sugiyama arXiv preprint arXiv:1901.10155, 2019	15	2019

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