Miao Xu
Miao Xu
RIKEN, AIP
Verified email at lamda.nju.edu.cn - Homepage
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, I Tsang, M Sugiyama
Advances in neural information processing systems, 8527-8537, 2018
2782018
Speedup matrix completion with side information: Application to multi-label learning
M Xu, R Jin, ZH Zhou
Advances in neural information processing systems, 2301-2309, 2013
1902013
On the flow characteristics of nanofluids by experimental approach and molecular dynamics simulation
W Cui, M Bai, J Lv, L Zhang, G Li, M Xu
Experimental Thermal and Fluid Science 39, 148-157, 2012
362012
Multi-label learning with PRO loss
M Xu, YF Li, ZH Zhou
Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence …, 2013
302013
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
192018
CUR algorithm for partially observed matrices
M Xu, R Jin, ZH Zhou
International Conference on Machine Learning, 1412-1421, 2015
192015
Incomplete Label Distribution Learning.
M Xu, ZH Zhou
IJCAI, 3175-3181, 2017
122017
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
82018
Progressive Identification of True Labels for Partial-Label Learning
J Lv, M Xu, L Feng, G Niu, X Geng, M Sugiyama
arXiv preprint arXiv:2002.08053, 2020
62020
Robust multi-label learning with PRO loss
M Xu, YF Li, ZH Zhou
IEEE Transactions on Knowledge and Data Engineering, 2019
52019
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
52018
Co-sampling: Training robust networks for extremely noisy supervision
B Han, Q Yao, X Yu, G Niu, M Xu, W Hu, IW Tsang, M Sugiyama
CoRR, abs, 1804
51804
Basic research on flow of nanofluids in cooling system of internal combustion engine
WZ Cui, ML Bai, JZ Lv, L Zhang, XJ Li, M Xu
Transactions of Chinese Society for Internal Combustion Engines 30 (1), 49-55, 2012
42012
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, 2020
22020
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
22019
Pumpout: A Meta Approach to Robust Deep Learning with Noisy Labels
B Han, G Niu, J Yao, X Yu, M Xu, I Tsang, M Sugiyama
arXiv preprint arXiv:1809.11008, 2018
22018
Pointwise Binary Classification with Pairwise Confidence Comparisons
L Feng, S Shu, N Lu, B Han, M Xu, G Niu, B An, M Sugiyama
arXiv preprint arXiv:2010.01875, 2020
2020
Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering
L Chen, Y Yao, F Xu, M Xu, H Tong
Advances in Neural Information Processing Systems 33, 2020
2020
Clipped Matrix Completion: A Remedy for Ceiling Effects
T Teshima, M Xu, I Sato, M Sugiyama
Proceedings of the AAAI Conference on Artificial Intelligence 33, 5151-5158, 2019
2019
A Pseudo-Label Method for Coarse-to-Fine Multi-Label Learning with Limited Supervision
CY Hsieh, M Xu, G Niu, HT Lin, M Sugiyama
2019
The system can't perform the operation now. Try again later.
Articles 1–20