[International Publication] {Native Publication}
[Journal Article] [Conference Paper]
B.-B. Jia, J.-Y. Liu, M.-L. Zhang. Instance-specific loss-weighted decoding for decomposition-based multi-class classification. IEEE Transactions on Neural Networks and Learning Systems, in press. [code]
J.-Y. Hang, M.-L. Zhang. Partial multi-label learning via label-specific feature corrections. Science China Information Sciences, in press. [appendix] [code]
J.-Y. Hang, M.-L. Zhang. Dual perspective of label-specific feature learning for multi-label classification. ACM Transactions on Knowledge Discovery from Data, in press. [conference version] [code]
Y. Gao, M. Xu, M.-L. Zhang. Complementary to multiple labels: A correlation-aware correction approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(12): 9179-9191. [code]
N. Xu, C. Qiao, Y. Zhao, X. Geng, M.-L. Zhang. Variational label enhancement for instance-dependent partial label learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(12): 11298-11313. [code]
S. Zhang, J.-Q. Li, H. Fujita, Y.-W. Li, D.-B. Wang, T.-T. Zhu, M.-L. Zhang, C.-Y. Liu. Student loss: Towards the probability assumption in inaccurate supervision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(6): 4460-4475. [code]
X.-R. Yu, D.-B. Wang, M.-L. Zhang. Dimensionality reduction for partial label learning: A unified and adaptive approach. IEEE Transactions on Knowledge and Data Engineering, 2024, 36(8): 3765-3782. [code]
H. Yang, Y.-Z. Jin, Z.-Y. Li, D.-B. Wang, X. Geng, M.-L. Zhang. Learning from noisy labels via dynamic loss thresholding. IEEE Transactions on Knowledge and Data Engineering, 2024, 36(11): 6503-6516. [code]
C. Si, Y. Jia, R. Wang, M.-L. Zhang, Y. Feng, C. Qu. Multi-label classification with high-rank and high-order label correlations. IEEE Transactions on Knowledge and Data Engineering, 2024, 36(8): 4076-4088. [code]
J. Zhang, T. Wei, M.-L. Zhang. Label-specific time-frequency energy-based neural network for instrument recognition. IEEE Transactions on Cybernetics, 2024, 54(11): 7080-7093. [code]
S. Shu, D.-B. Wang, S. Yuan, H. Wei, J. Jiang, L. Feng, M.-L. Zhang. Multiple-instance learning from triplet comparison bags. ACM Transactions on Knowledge Discovery from Data, 2024, 18(4): Article 90. [code]
X.-R. Yu, D.-B. Wang, M.-L. Zhang. Partial label learning with emerging new labels. Machine Learning, 2024, 113(4): 1549-1565. [code]
W. Tang, W. Zhang, M.-L. Zhang. Multi-instance partial-label learning: Towards exploiting dual inexact supervision. Science China Information Sciences, 2024, 67(3): Article 132103. [data] [code]
B.-Q. Liu, B.-B. Jia, M.-L. Zhang. Towards enabling binary decomposition for partial multi-label learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(11): 13203-13217. [code]
N. Xu, J. Shu, R. Zheng, X. Geng, D. Meng, M.-L. Zhang. Variational label enhancement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(5): 6537 - 6551. [code]
B.-B. Jia, M.-L. Zhang. Multi-dimensional classification via decomposed label encoding. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(2): 1844-1856. [code] [data]
B.-B. Jia, M.-L. Zhang. Multi-dimensional multi-label classification: Towards encompassing heterogeneous label spaces and multi-label annotations. Pattern Recognition, 2023, 138: Article 109357. [code]
B.-B. Jia, J.-Y. Liu, J.-Y. Hang, M.-L. Zhang. Learning label-specific features for decomposition-based multi-class classification. Frontiers of Computer Science, 2023, 17(6): Article 176348. [code]
J.-Y. Hang, M.-L. Zhang. Collaborative learning of label semantics and deep label-specific features for multi-label classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(12): 9860-9871. [code]
D.-B. Wang, M.-L. Zhang, L. Li. Adaptive graph guided disambiguation for partial label learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(12): 8796-8811. [Supplement] [code]
Z.-B. Yu, M.-L. Zhang. Multi-label classification with label-specific feature generation: A wrapped approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(9): 5199-5210. [code]
M.-L. Zhang, J.-H. Wu, W.-X. Bao. Disambiguation enabled linear discriminant analysis for partial label dimensionality reduction. ACM Transactions on Knowledge Discovery from Data, 2022, 16(4): Article 72. [Conference version] [code]
Y. Zhang, W. Fan, Q. Hao, X. Wu, M.-L. Zhang. CAFE and SOUP: Towards adaptive VDI workload prediction. ACM Transactions on Intelligent Systems and Technology, 2022, 13(6): Article 94. [Conference version]
M.-L. Zhang, Y.-K. Li, H. Yang, X.-Y. Liu. Towards class-imbalance aware multi-label learning. IEEE Transactions on Cybernetics, 2022, 52(6): 4459-4471. [Conference version] [code]
B.-B. Jia, M.-L. Zhang. Maximum margin multi-dimensional classification. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(12): 7185-7198. [Conference version] [code] [data]
B.-B. Jia, M.-L. Zhang. Decomposition-based classifier chains for multi-dimensional classification. IEEE Transactions on Artificial Intelligence, 2022, 3(2): 176-191. [code] [data]
Y.-B. Wang, J.-Y. Hang, M.-L. Zhang. Stable label-specific features generation for multi-label learning via mixture-based clustering ensemble. IEEE/CAA Journal of Automatica Sinica, 2022, 9(7): 1248-1261. [code]
B.-B. Jia, M.-L. Zhang. Multi-dimensional classification via selective feature augmentation. Machine Intelligence Research, 2022, 19(1): 38-51. [code]
M.-L. Zhang, J.-P. Fang. Partial multi-label learning via credible label elicitation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(10): 3587-3599. [Conference version] [code] [data]
M.-L. Zhang, Q.-W. Zhang, J.-P. Fang, Y.-K. Li, X. Geng. Leveraging implicit relative labeling-importance information for effective multi-label learning. IEEE Transactions on Knowledge and Data Engineering, 2021, 33(5): 2057-2070. [Conference version] [code]
M.-L. Zhang, J.-P. Fang, Y.-B. Wang. BiLabel-specific features for multi-label classification. ACM Transactions on Knowledge Discovery from Data, 2021, 16(1): Article 18. [code]
Y.-P. Sun, M.-L. Zhang. Compositional metric learning for multi-label classification. Frontiers of Computer Science, 2021, 15(5): Article 155320. [code]
B.-B. Jia, M.-L. Zhang. Multi-dimensional classification via stacked dependency exploitation. Science China Information Sciences, 2020, 63(12): Article 222102. [code] [data]
B.-B. Jia, M.-L. Zhang. Multi-dimensional classification via kNN feature augmentation. Pattern Recognition, 2020, 106: Article 107423. [Conference version] [code] [data]
Y. Zhang, Y. Wang, X.-Y. Liu, S. Mi, M.-L. Zhang. Large-scale multi-label classification using unknown streaming images. Pattern Recognition, 2020, 99: Article 107100.
M. Huang, F. Zhuang, X. Zhang, X. Ao, Z. Niu, M.-L. Zhang, Q. He. Supervised representation learning for multi-label classification. Machine Learning, 2019, 108(5): 747-763.
D. Zhou, Z. Zhang, M.-L. Zhang, Y. He. Weakly supervised POS tagging without disambiguation. ACM Transactions on Asian and Low-Resource Language Information Processing, 2018, 17(4): Article 35.
M.-L. Zhang, Y.-K. Li, X.-Y. Liu, X. Geng. Binary relevance for multi-label learning: An overview. Frontiers of Computer Science, 2018, 12(2): 191-202.
M.-L. Zhang, F. Yu, C.-Z. Tang. Disambiguation-free partial label learning. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(10): 2155-2167. [Conference version] [data] [code]
F. Yu, M.-L. Zhang. Maximum margin partial label learning. Machine Learning, 2017, 106(4): 573-593. [Conference version] [data] [code]
M.-L. Zhang, L. Wu. LIFT: Multi-label learning with label-specific features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(1): 107-120. [Conference version] [code]
M.-L. Zhang, Z.-H. Zhou. A review on multi-label learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(8): 1819-1837. [Longer version]
M.-L. Zhang, Z.-H. Zhou. Exploiting unlabeled data to enhance ensemble diversity. Data Mining and Knowledge Discovery, 2013, 26(1): 98-129. [Conference version] [code]
Z.-H. Zhou, M.-L. Zhang, S.-J. Huang, Y.-F. Li. Multi-instance multi-label learning. Artificial Intelligence, 2012, 176(1): 2291-2320. [code] (CORR abs/1005.1545)
M.-L. Zhang, Z.-H. Zhou. CoTrade: Confident co-training with data editing. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2011, 41(6): 1612-1626. [code]
M.-L. Zhang, J. M. Peña, V. Robles. Feature selection for multi-label naive bayes classification. Information Sciences, 2009, 179(19): 3218-3229. [code]
M.-L. Zhang, Z.-H. Zhou. Multi-instance clustering with applications to multi-instance prediction. Applied Intelligence, 2009, 31(1): 47-68. [code]
M.-L. Zhang, Z.-J. Wang. MIMLRBF: RBF neural networks for multi-instance multi-label learning. Neurocomputing, 2009, 72(16-18): 3951-3956. [code] [image data] [retuers data]
M.-L. Zhang. ML-RBF: RBF neural networks for multi-label learning. Neural Processing Letters, 2009, 29(2): 61-74. [code]
M.-L. Zhang, Z.-H. Zhou. ML-kNN: a lazy learning approach to multi-label learning. Pattern Recognition, 2007, 40(7): 2038-2048. [code] [Yeast data] [image data] [Yahoo data: original version preprocessed version]
Z.-H. Zhou, M.-L. Zhang. Solving multi-instance problems with classifier ensemble based on constructive clustering. Knowledge and Information Systems, 2007, 11(2): 155-170. [code]
M.-L. Zhang, Z.-H. Zhou. Multi-label neural networks with applications to functional genomics and text categorization. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(10): 1338-1351. [code] [Yeast data] [Reuters corpus]
M.-L. Zhang, Z.-H. Zhou. Adapting RBF neural networks to multi-instance learning. Neural Processing Letters, 2006, 23(1): 1-26. [code]
M.-L. Zhang, Z.-H. Zhou. Improve multi-instance neural networks through feature selection. Neural Processing Letters, 2004, 19(1): 1-10. [code] (11Kb) [TechReport for BP-MIP]
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Y.-Z. Wang, W. Zhang, M.-L. Zhang. Partial label causal representation learning for instance-dependent supervision and domain generalization. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25), in press. [code]
W. Chen, J.-X. Mao, M.-L. Zhang. Learnware specification via label-aware neural embedding. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25), in press. [code]
Y.-F. Yang, W. Tang, M.-L. Zhang. Fast multi-instance partial-label learning. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25), in press. [appendix] [code]
J.-X. Mao, Y. Rui, M.-L. Zhang. Implicit relative labeling-importance aware multi-label metric learning. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25), in press. [appendix] [code]
Y.-Y. Zhang, B.-B. Jia, M.-L. Zhang. Evolutionary classifier chain for multi-dimensional classification. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25), in press. [appendix] [code]
Y.-F. Zhang, M.-L. Zhang. Generalization analysis for label-specific representation learning. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), Vancouver, Canada, 2024, in press.
J. Lv, Y. Liu, S. Xia, N. Xu, M. Xu, G. Niu, M.-L. Zhang, M. Sugiyama, X. Geng. What makes partial-label learning algorithms effective? In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), Vancouver, Canada, 2024, in press.
W. Tang, Y.-F. Yang, Z. Wang, W. Zhang, M.-L. Zhang. Multi-instance partial-label learning with margin adjustment. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), Vancouver, Canada, 2024, in press. [code]
Y.-B. Wang, J.-Y. Hang, M.-L. Zhang. Multi-label open set recognition. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), Vancouver, Canada, 2024, in press. [code]
T. Wei, H.-T. Li, C.-S. Li, J.-X. Shi, Y.-F. Li, M.-L. Zhang. Vision-language models are strong noisy label detectors. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), Vancouver, Canada, 2024, in press. [code]
Z.-H. Zhou, S. Fang, Z.-J. Zhou, T. Wei, Y. Wan, M.-L. Zhang. Continuous contrastive learning for long-tailed semi-supervised recognition. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), Vancouver, Canada, 2024, in press. [code]
Y.-F. Yang, W. Tang, M.-L. Zhang. ProMIPL: A probabilistic generative model for multi-instance partial-label learning. In: Proceedings of the 24th IEEE International Conference on Data Mining (ICDM'24), Abu Dhabi, UAE, 2024, in press. [code]
Y. Sun, X. Li, Q. Sun, M.-L. Zhang, Z. Ren. Improved weighted tensor schatten p-norm for fast multi-view graph clustering. In: Proceedings of the 32nd ACM International Conference on Multimedia (MM'24), Melbourne, Australia, 2024, 1427-1436. [code]
D.-B. Wang, M.-L. Zhang. Calibration bottleneck: Over-compressed representations are less calibratable. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), Vienna, Austria, 2024, 52156-52170. [code]
T. Wei, Z. Mao, Z.-H. Zhou, Y. Wan, M.-L. Zhang. Learning label shift correction for test-agnostic long-tailed recognition. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), Vienna, Austria, 2024, 52611-52631. [code]
J.-Y. Hang, M.-L. Zhang. Binary decomposition: A problem transformation perspective for open-set semi-supervised learning. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), Vienna, Austria, 2024, 17505-17518. [code]
Y.-F. Zhang, M.-L. Zhang. Generalization analysis for multi-label learning. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), Vienna, Austria, 2024, 60220-60243.
J.-X. Mao, J.-Y. Hang, M.-L. Zhang. Learning label-specific multiple local metrics for multi-label classification. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), Jeju, South Korea, 2024, 4742-4750. [appendix] [code]
T. Huang, B.-B. Jia, M.-L. Zhang. Deep multi-dimensional classification with pairwise dimension-specific features. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), Jeju, South Korea, 2024, 4183-4191. [appendix] [code]
Y. Tang, Y. Gao, Y.-G. Luo, J.-C. Yang, M. Xu, M.-L. Zhang. Unlearning from weakly supervised learning. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), Jeju, South Korea, 2024, 5000-5008. [appendix] [code]
B. Ye, K. Gan, T. Wei, M.-L. Zhang. Bridging the gap: Learning pace synchronization for open-world semi-supervised learning. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), Jeju, South Korea, 2024, 5362-5370. [appendix] [code]
W. Tang, W. Zhang, M.-L. Zhang. Exploiting conjugate label information for multi-instance partial-label learning. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), Jeju, South Korea, 2024, 4973-4981. [appendix] [code]
D.-D. Wu, C. Fu, W. Wu, W. Xia, X. Zhang, J. Zhou, M.-L. Zhang. Efficient model stealing defense with noise transition matrix. In: Proceedings of the 35th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'24), Seattle, WA, 2024, 24305-24315. [appendix] [code]
W.-X. Bao, Y. Rui, M.-L. Zhang. Disentangled partial label learning. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), Vancouver, Canada, 2024, 11007-11015. [appendix] [code]
T. Wei, B.-L. Wang, M.-L. Zhang. EAT: Towards long-tailed out-of-distribution detection. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), Vancouver, Canada, 2024, 15787-15795. [code]
Y. Jia, X. Peng, R. Wang, M.-L. Zhang. Long-tailed partial label learning by head classifier and tail classifier cooperation. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), Vancouver, Canada, 2024, 12857-12865. [code]
D.-D. Wu, D.-B. Wang, M.-L. Zhang. Distilling reliable knowledge for instance-dependent partial label learning. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), Vancouver, Canada, 2024, 15888-15896. [appendix] [code]
J.-Y. Hang, M.-L. Zhang. Partial multi-label learning with probabilistic graphical disambiguation. In: Advances in Neural Information Processing Systems 36 (NeurIPS'23), New Orleans, LA, 2023, 1339-1351. [appendix] [code]
W. Wang, L. Feng, Y. Jiang, G. Niu, M.-L. Zhang, M. Sugiyama. Binary classification with confidence difference. In: Advances in Neural Information Processing Systems 36 (NeurIPS'23), New Orleans, LA, 2023, 5936-5960. [appendix] [code]
W. Tang, W. Zhang, M.-L. Zhang. Disambiguated attention embedding for multi-instance partial-label learning. In: Advances in Neural Information Processing Systems 36 (NeurIPS'23), New Orleans, LA, 2023, 56756-56771. [appendix] [code] [data]
Y.-F. Zhang, M.-L. Zhang. Nearly-tight bounds for deep kernel learning. In: Proceedings of the 40th International Conference on Machine Learning (ICML'23), Honolulu, HI, 2023, 41861-41879.
T. Huang, B.-B. Jia, M.-L. Zhang. Progressive label propagation for semi-supervised multi-dimensional classification. In: Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI'23), Macau, China, 2023, 3821-3829. [Supplement] [code]
Y. Gao, M. Xu, M.-L. Zhang. Unbiased risk estimator to multi-labeled complementary label learning. In: Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI'23), Macau, China, 2023, 3732-3740. [Supplement] [code]
J.-X. Mao, W. Wang, M.-L. Zhang. Label specific multi-semantics metric learning for multi-label classification: Global consideration helps. In: Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI'23), Macau, China, 2023, 4055-4063. [Supplement] [code]
H.-T. Li, T. Wei, H. Yang, K. Hu, C. Peng, L.-B. Sun, X.-L. Cai, M.-L. Zhang. Stochastic feature averaging for learning with long-tailed noisy labels. In: Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI'23), Macau, China, 2023, 3902-3910. [Supplement] [code]
Y. Jia, C. Si, M.-L. Zhang. Complementary classifier induced partial label learning. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'23), Long Beach, CA, 2023, 974-983. [code]
R.-J. Dong, J.-Y. Hang, T. Wei, M.-L. Zhang. Can label-specific features help partial-label learning? In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), Washington D.C., 2023, 7432-7440. [code]
X. Cheng, D.-B. Wang, L. Feng, M.-L. Zhang, B. An. Partial-label regression. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), Washington D.C., 2023, 7140-7147. [appendix] [code]
D.-B. Wang, L. Li, P. Zhao, P.-A. Heng, M.-L. Zhang. On the pitfall of mixup for uncertainty calibration. In: Proceedings of the 34th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'23), Vancouver, Canada, 2023, 7609-7618. [appendix] [code]
N. Xu, C. Qiao, J. Lv, X. Geng, M.-L. Zhang. One positive label is sufficient: Single-positive multi-label learning with label enhancement. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), New Orleans, LA, 2022, 21765-21776. [appendix] [code]
W. Zhang, X. Zhang, H.-W. Deng, M.-L. Zhang. Multi-instance causal representation learning for instance label prediction and out-of-distribution generalization. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), New Orleans, LA, 2022, 34940-34953. [code]
W.-X. Bao, J.-Y. Hang, M.-L. Zhang. Submodular feature selection for partial label learning. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'22), Washington D. C., 2022, 26–34. [code]
W. Wang, M.-L. Zhang. Partial label learning with discrimination augmentation. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'22), Washington D. C., 2022, 1920–1928. [code]
D.-D. Wu, D.-B. Wang, M.-L. Zhang. Revisiting consistency regularization for deep partial label learning. In: Proceedings of the 39th International Conference on Machine Learning (ICML'22), Baltimore, MD, 2022, 24212-24225. [code]
J.-Y. Hang, M.-L. Zhang. Dual perspective of label-specific feature learning for multi-label classification. In: Proceedings of the 39th International Conference on Machine Learning (ICML'22), Baltimore, MD, 2022, 8375-8386. [code]
T. Wei, J.-X. Shi, Y.-F. Li, M.-L. Zhang. Protoypical classifier for robust class-imbalanced learning. In: Proceedings of the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'22), Chengdu, China, 2022, 44-57.
J.-Y. Hang, M.-L. Zhang, Y. Feng, X. Song. End-to-end probabilistic label-specific feature learning for multi-label classification. In: Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI'22), Virtual Event, 2022, 6847-6855. [code] [Supplement]
D.-B. Wang, L. Feng, M.-L. Zhang. Rethinking calibration of deep neural networks: Do not be afraid of overconfidence. In: Advances in Neural Information Processing Systems 34 (NeurIPS'21), Virtual Conference, 2021, 11809-11820. [appendix] [code]
N. Xu, C. Qiao, X. Geng, M.-L. Zhang. Instance-dependent partial label learning. In: Advances in Neural Information Processing Systems 34 (NeurIPS'21), Virtual Conference, 2021, 27119-27130. [appendix] [code]
W.-X. Bao, J.-Y. Hang, M.-L. Zhang. Partial label dimensionality reduction via confidence-based dependence maximization. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'21), Virtual Event, 46-54. [code]
J. Wang, D. Deng, X. Xie, X. Shu, Y.-X. Huang, L.-W. Cai, H. Zhang, M.-L. Zhang, Z.-H. Zhou, Y. Wu. Tac-Valuer: Knolwedge-based stroke evaluation in table tennis. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'21, ADS track), Virtual Event, 3688-3696.
B.-B. Jia, M.-L. Zhang. Multi-dimensional classification via sparse label encoding. In: Proceedings of the 38th International Conference on Machine Learning (ICML'21), Virtual Conference, 2021, 4917-4926. [code] [data]
Y. Gao, M.-L. Zhang. Discriminative complementary-label learning with weighted loss. In: Proceedings of the 38th International Conference on Machine Learning (ICML'21), Virtual Conference, 2021, 3587-3597. [code]
D.-B. Wang, L. Feng, M.-L. Zhang. Learning from complementary labels via partial-output consistency regularization. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'21), Virtual Conference, 2021, 3075-3081. [code]
W. Fan, Y. Zhang, Q. Hao, X. Wu, M.-L. Zhang. BAMBOO: A multi-instance multi-label approach towards VDI user logon behavior modeling. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'21), Virtual Conference, 2021, 2367-2373.
Q.-W. Zhang, X. Zhang, Z. Yan, R. Liu, Y. Cao, M.-L. Zhang. Correlation-guided representation for multi-label text classification. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'21), Virtual Conference, 2021, 3363-3369. [code]
D.-B. Wang, Y. Wen, L. Pan, M.-L. Zhang. Learning from noisy labels with complementary loss functions. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI'21), Virtual Event, 2021, 10111-10119. [code] [Supplement]
Z.-R. Zhang, Q.-W. Zhang, Y. Cao, M.-L. Zhang. Exploiting unlabeled data via partial label assignment for multi-class semi-supervised learning. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI'21), Virtual Event, 2021, 10973-10980. [code]
W. Wang, M.-L. Zhang. Semi-supervised partial label learning via confidence-rated margin maximization. In: Advances in Neural Information Processing Systems 33 (NeurIPS'20), Vancouver, Canada, 2020, 6982-6993. [code]
J.-H. Wu, X. Wu, Q.-G. Chen, Y. Hu, M.-L. Zhang. Feature-induced manifold disambiguation for multi-view partial multi-label learning. In: Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'20), Virtual Event, 2020, 557-565. [code]
B.-B. Jia, M.-L. Zhang. Maximum margin multi-dimensional classification. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York, NY, 2020, 4312-4319. [code] [data]
Z.-S. Chen, X. Wu, Q.-G. Chen, Y. Hu, M.-L. Zhang. Multi-view partial multi-label learning with graph-based disambiguation. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York, NY, 3553-3560. [code]
B.-B. Jia, M.-L. Zhang. MD-kNN: An instance-based approach for multi-dimensional classification. In: Proceedings of the 25th International Conference on Pattern Recognition (ICPR'20), Milan, Italy, 126-133. [code] [data]
J.-H. Wu, M.-L. Zhang. Disambiguation enabled linear discriminant analysis for partial label dimensionality reduction. In: Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'19), Anchorage, AK, 2019, 416-424. [code]
D.-B. Wang, L. Li, M.-L. Zhang. Adaptive graph guided disambiguation for partial label learning. In: Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'19), Anchorage, AK, 2019, 83-91. [code]
X. Wu, Q.-G. Chen, Y. Hu, D.-B. Wang, X. Chang, X. Wang, M.-L. Zhang. Multi-view multi-label learning with view-specific information extraction. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), Macau, China, 2019, 3884-3890. [code]
Z.-S. Chen, M.-L. Zhang. Multi-Label learning with regularization enriched label-specific features. In: Proceedings of the 11th Asian Conference on Machine Learning (ACML'19), Nagoya, Japan, 2019, 411-424.
Y. Zhang, W.-P. Fan, X. Wu, H. Chen, B.-Y. Li, M.-L. Zhang. CAFE: Adaptive VDI workload prediction with multi-grained features. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019, 5821-5828.
B.-B. Jia, M.-L. Zhang. Multi-dimensional classification via kNN feature augmentation. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019, 3975-3982. [code] [data]
J.-P. Fang, M.-L. Zhang. Partial multi-label learning via credible label elicitation. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019, 3518-3525. [code]
J. Wang, M.-L. Zhang. Towards mitigating the class-imbalance problem for partial label learning. In: Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'18), London, UK, 2018, 2427-2436. [code]
S.-Y. Ding, X.-Y. Liu, M.-L. Zhang. Imbalanced augmented class learning with unlabeled data by label confidence propagation. In: Proceedings of the 18th IEEE International Conference on Data Mining (ICDM'18), Singapore, 2018, 79-88.
X. Wu, M.-L. Zhang. Towards enabling binary decomposition for partial label learning. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, 2868-2974. [code]
Q.-W. Zhang, Y. Zhong, M.-L. Zhang. Feature-induced labeling information enrichment for multi-label learning. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), New Orleans, LA, 2018, 4446-4453. [code]
W. Zhan, M.-L. Zhang. Inductive semi-supervised multi-label learning with co-training. In: Proceedings of the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'17), Halifax, Canada, 2017, 1305-1314. [code]
W.-J. Zhou, Y. Yu, M.-L. Zhang. Binary linear compression for multi-label classification. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, 3546-3552.
C.-Z. Tang, M.-L. Zhang. Confidence-rated discriminative partial label learning. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017, 2611-2617. [data] [code]
M.-L. Zhang, B.-B. Zhou, X.-Y. Liu. Partial label learning via feature-aware disambiguation. In: Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'16), San Francisco, CA, 2016, 1335-1344. [data] [code]
P. Hou, X. Geng, M.-L. Zhang. Multi-label manifold learning. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 2016, 1680-1686. [code]
F. Yu, M.-L. Zhang. Maximum margin partial label learning. In: Proceedings of the 7th Asian Conference on Machine Learning (ACML'15), Hong Kong, China, 2015, 96-111. [data] [code]
M.-L. Zhang, F. Yu. Solving the partial label learning problem: An instance-based approach. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, 2015, 4048-4054. [data] [code]
M.-L. Zhang, Y.-K. Li, X.-Y. Liu. Towards class-imbalance aware multi-label learning. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, 2015, 4041-4047. [code]
Y.-K. Li, M.-L. Zhang, X. Geng. Leveraging implicit relative labeling-importance information for effective multi-label learning. In: Proceedings of the 15th IEEE International Conference on Data Mining (ICDM'15), Atlantic City, NJ, 2015, 251-260. [code]
M.-L. Zhang. Disambiguation-free partial label learning. In: Proceedings of the 14th SIAM International Conference on Data Mining (SDM'14), Philadelphia, PA, 2014, 37-45. [data] [code]
L. Wu, M.-L. Zhang. Multi-label classification with unlabeled data: An inductive approach. In: Proceedings of the 5th Asian Conference on Machine Learning (ACML'13), Canberra, Australia, 2013, 197-212. [code]
M.-L. Zhang. LIFT: Multi-label learning with label-specific features. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI'11), Barcelona, Spain, 2011, 1609-1614. (poster) [code]
M.-L. Zhang, Z.-H. Zhou. Exploiting unlabeled data to enhance ensemble diversity. In: Proceedings of the 10th IEEE International Conference on Data Mining (ICDM'10), Sydney, Australia, 2010, 619-628. [code] (CORR abs/0909.3593)
M.-L. Zhang, K. Zhang. Multi-label learning by exploiting label dependency. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'10), Washington D. C., 2010, 999-1007. [code]
M.-L. Zhang. A k-nearest neighbor based multi-instance multi-label learning algorithm. In: Proceedings of the 22nd International Conference on Tools with Artificial Intelligence (ICTAI'10), Arras, France, 2010, 207-212. [code]
M.-L. Zhang. Generalized multi-instance learning: problems, algorithms and data sets. In: Proceedings of the 2009 Global Congress on Intelligent Systems, vol. III (GCIS'09), Xiamen, China, 2009, 539-543.
M.-L. Zhang, Z.-H. Zhou. M3MIML: A maximum margin method for multi-instance multi-label learning. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08), Pisa, Italy, 2008, 688-697. [code] [image data] [retuers data]
M.-L. Zhang, Z.-H. Zhou. Multi-label learning by instance differentiation. In: Proceedings of the 22nd Conference on Artificial Intelligence (AAAI'07), Vancouver, Canada, 2007, 669-674. [code]
Z.-H. Zhou, M.-L. Zhang. Multi-instance multi-label learning with application to scene classification. In: Advances in Neural Information Processing Systems 19 (NIPS'06), Vancouver, Canada, 2007, 1609-1616. [code] [image data]
M.-L. Zhang, Z.-H. Zhou. A k-nearest neighbor based algorithm for multi-label classification. In: Proceedings of the 1st IEEE International Conference on Granular Computing (GrC'05), Beijing, 2005, 718-721. [code]
M.-L. Zhang, Z.-H. Zhou. Ensembles of multi-instance neural networks. In: Proceedings of the International Conference on Intelligent Information Processing (ICIIP'04), Beijing, 2004, 471-474.
Z.-H. Zhou, M.-L. Zhang. Ensembles of multi-instance learners. In: Lavrač N, Gamberger D, Blockeel H, Todorovski L, Eds. Lecture Notes in Artificial Intelligence 2837 (ECML'03), Berlin: Springer-Verlag, 2003, 492-502. [code]
Z.-H. Zhou, M.-L. Zhang, Chen K-J. A novel bag generator for image database retrieval with multi-instance learning techniques. In: Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'03), Sacramento, CA, 2003, 565-569.
Z.-H. Zhou, M.-L. Zhang. Neural networks for multi-instance learning. In: Proceedings of the International Conference on Intelligent Information Technology (ICIIT'02), Beijing, 2002, 455-459.
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