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Ming-Kun Xie
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2020 – today
- 2024
- [j5]Haochen Shi, Ming-Kun Xie, Shengjun Huang:
Robust AUC maximization for classification with pairwise confidence comparisons. Frontiers Comput. Sci. 18(4) (2024) - [j4]Feng Sun, Ming-Kun Xie, Sheng-Jun Huang
:
A Deep Model for Partial Multi-label Image Classification with Curriculum-based Disambiguation. Mach. Intell. Res. 21(4): 801-814 (2024) - [j3]Jia-Yao Chen
UNM: A Universal Approach for Noisy Multi-Label Learning. IEEE Trans. Knowl. Data Eng. 36(9): 4968-4980 (2024) - [c16]Wenhai Wan, Xinrui Wang, Ming-Kun Xie, Shao-Yuan Li, Sheng-Jun Huang, Songcan Chen:
Unlocking the Power of Open Set: A New Perspective for Open-Set Noisy Label Learning. AAAI 2024: 15438-15446 - [c15]Chen-Chen Zong, Ye-Wen Wang, Ming-Kun Xie, Sheng-Jun Huang:
Dirichlet-Based Prediction Calibration for Learning with Noisy Labels. AAAI 2024: 17254-17262 - [c14]Jiahao Xiao, Ming-Kun Xie
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-supervised Multi-label Learning. ECCV (52) 2024: 437-454 - [c13]Ye-Wen Wang, Chen-Chen Zong, Ming-Kun Xie, Sheng-Jun Huang:
Dirichlet-Based Coarse-to-Fine Example Selection For Open-Set Annotation. ICME 2024: 1-6 - [c12]Ming-Kun Xie, Jiahao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. ICML 2024 - [c11]Hao-Zhe Liu
Asymmetric Beta Loss for Evidence-Based Safe Semi-Supervised Multi-Label Learning. KDD 2024: 1909-1920 - [i11]Chen-Chen Zong, Ye-Wen Wang, Ming-Kun Xie, Sheng-Jun Huang:
Dirichlet-Based Prediction Calibration for Learning with Noisy Labels. CoRR abs/2401.07062 (2024) - [i10]Ming-Kun Xie, Jiahao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. CoRR abs/2404.06287 (2024) - [i9]Jiahao Xiao, Ming-Kun Xie, Heng-Bo Fan, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning. CoRR abs/2407.18624 (2024) - 2023
- [j2]Ming-Kun Xie
CCMN: A General Framework for Learning With Class-Conditional Multi-Label Noise. IEEE Trans. Pattern Anal. Mach. Intell. 45(1): 154-166 (2023) - [c10]Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Multi-Label Knowledge Distillation. ICCV 2023: 17225-17234 - [c9]Ming-Kun Xie, Jiahao Xiao, Hao-Zhe Liu, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning. NeurIPS 2023 - [i8]Ming-Kun Xie, Jiahao Xiao, Hao-Zhe Liu, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning. CoRR abs/2305.02795 (2023) - [i7]Wenhai Wan, Xinrui Wang, Ming-Kun Xie, Shengjun Huang, Songcan Chen, Shaoyuan Li:
Unlocking the Power of Open Set : A New Perspective for Open-set Noisy Label Learning. CoRR abs/2305.04203 (2023) - [i6]Penghui Yang
Multi-Label Knowledge Distillation. CoRR abs/2308.06453 (2023) - 2022
- [j1]Ming-Kun Xie
Partial Multi-Label Learning With Noisy Label Identification. IEEE Trans. Pattern Anal. Mach. Intell. 44(7): 3676-3687 (2022) - [c8]Ming-Kun Xie, Jiahao Xiao, Sheng-Jun Huang:
Label-Aware Global Consistency for Multi-Label Learning with Single Positive Labels. NeurIPS 2022 - [i5]Feng Sun, Ming-Kun Xie, Sheng-Jun Huang:
A Deep Model for Partial Multi-Label Image Classification with Curriculum Based Disambiguation. CoRR abs/2207.02410 (2022) - [i4]Bo-Shi Zou, Ming-Kun Xie, Sheng-Jun Huang:
Meta Objective Guided Disambiguation for Partial Label Learning. CoRR abs/2208.12459 (2022) - [i3]Chen-Chen Zong, Zheng-Tao Cao, Hong-Tao Guo, Yun Du, Ming-Kun Xie, Shao-Yuan Li, Sheng-Jun Huang:
Noise-Robust Bidirectional Learning with Dynamic Sample Reweighting. CoRR abs/2209.01334 (2022) - 2021
- [c7]Ming-Kun Xie, Feng Sun, Sheng-Jun Huang:
Partial Multi-Label Learning with Meta Disambiguation. KDD 2021: 1904-1912 - [c6]Ming-Kun Xie, Sheng-Jun Huang:
Multi-Label Learning with Pairwise Relevance Ordering. NeurIPS 2021: 23545-23556 - [i2]Ming-Kun Xie, Sheng-Jun Huang:
CCMN: A General Framework for Learning with Class-Conditional Multi-Label Noise. CoRR abs/2105.07338 (2021) - 2020
- [c5]Ming-Kun Xie, Sheng-Jun Huang:
Partial Multi-Label Learning with Noisy Label Identification. AAAI 2020: 6454-6461 - [c4]Ming-Kun Xie, Sheng-Jun Huang:
Semi-Supervised Partial Multi-Label Learning. ICDM 2020: 691-700
2010 – 2019
- 2019
- [c3]Ming-Kun Xie, Sheng-Jun Huang:
Learning Class-Conditional GANs with Active Sampling. KDD 2019: 998-1006 - 2018
- [c2]Ming-Kun Xie, Sheng-Jun Huang:
Partial Multi-Label Learning. AAAI 2018: 4302-4309 - [c1]Sheng-Jun Huang, Miao Xu
Active Feature Acquisition with Supervised Matrix Completion. KDD 2018: 1571-1579 - [i1]Sheng-Jun Huang, Miao Xu, Ming-Kun Xie, Masashi Sugiyama, Gang Niu, Songcan Chen:
Active Feature Acquisition with Supervised Matrix Completion. CoRR abs/1802.05380 (2018)
Coauthor Index
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