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Miao Xu
Miao Xu
Verified email at uq.edu.au - 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 31, 2018
10492018
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
2612013
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
672020
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
622020
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
412020
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
342018
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
332013
Incomplete Label Distribution Learning.
M Xu, ZH Zhou
IJCAI, 3175-3181, 2017
322017
CUR algorithm for partially observed matrices
M Xu, R Jin, ZH Zhou
International Conference on Machine Learning, 1412-1421, 2015
282015
Learning from group supervision: the impact of supervision deficiency on multi-label learning
M Xu, LZ Guo
Science China Information Sciences 64 (3), 1-13, 2021
152021
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
152019
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
152018
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
122018
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
102018
Positive-Unlabeled Learning from Imbalanced Data.
G Su, W Chen, M Xu
IJCAI, 2995-3001, 2021
82021
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
72021
A pseudo-label method for coarse-to-fine multi-label learning with limited supervision
CY Hsieh, M Xu, G Niu, HT Lin, M Sugiyama
62019
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
62019
Self-Supervised Adversarial Distribution Regularization for Medication Recommendation.
Y Wang, W Chen, D Pi, L Yue, S Wang, M Xu
IJCAI, 3134-3140, 2021
52021
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 (01), 5151-5158, 2019
42019
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