| 2026 | AAAI | Collaborative Dual Representations for Semi-Supervised Partial Label Learning. | Wei-Xuan Bao, Yong Rui, Min-Ling Zhang |
| 2026 | AAAI | APVR: Hour-Level Long Video Understanding with Adaptive Pivot Visual Information Retrieval. | Hong Gao, Yiming Bao, Xuezhen Tu, Bin Zhong, Linan Yue, Min-Ling Zhang |
| 2026 | AAAI | Classifier-induced Reciprocal Points for Multi-label Open-set Recognition. | Yibo Wang, Yong Rui, Min-Ling Zhang |
| 2026 | ESANN | Multi-label Complementary Labels Learning under Hard Logical Constraints. | Luca Oneto, Yi Gao, Davide Anguita, Fabio Roli, Min-Ling Zhang, Fulvio Mastrogiovanni |
| 2025 | AAAI | Learnware Specification via Label-Aware Neural Embedding. | Wei Chen, Junxiang Mao, Min-Ling Zhang |
| 2025 | AAAI | Implicit Relative Labeling-Importance Aware Multi-Label Metric Learning. | Junxiang Mao, Yong Rui, Min-Ling Zhang |
| 2025 | AAAI | Partial Label Causal Representation Learning for Instance-Dependent Supervision and Domain Generalization. | Yizhi Wang, Weijia Zhang, Min-Ling Zhang |
| 2025 | AAAI | Fast Multi-Instance Partial-Label Learning. | Yin-Fang Yang, Wei Tang, Min-Ling Zhang |
| 2025 | AAAI | Evolutionary Classifier Chain for Multi-Dimensional Classification. | Yu-Yang Zhang, Bin-Bin Jia, Min-Ling Zhang |
| 2025 | AISTATS | HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks. | Xin Liu, Weijia Zhang, Min-Ling Zhang |
| 2025 | CHI | ZzzMate: A Self-Conscious Emotion-Aware Chatbot for Sleep Intervention. | Xiao Tang, Zhuying Li, Xin Sun, Xuhai Xu, Min-Ling Zhang |
| 2025 | ICLR | Realistic Evaluation of Deep Partial-Label Learning Algorithms. | Wei Wang, Dong-Dong Wu, Jindong Wang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama |
| 2025 | ICLR | Semi-Supervised CLIP Adaptation by Enforcing Semantic and Trapezoidal Consistency. | Kai Gan, Bo Ye, Min-Ling Zhang, Tong Wei |
| 2025 | ICLR | Noise Separation guided Candidate Label Reconstruction for Noisy Partial Label Learning. | Xiaorui Peng, Yuheng Jia, Fuchao Yang, Ran Wang, Min-Ling Zhang |
| 2025 | ICML | Towards Escaping from Class Dependency Modeling for Multi-Dimensional Classification. | Teng Huang, Bin-Bin Jia, Min-Ling Zhang |
| 2025 | ICML | Learnware Specification via Dual Alignment. | Wei Chen, Junxiang Mao, Xiaozheng Wang, Min-Ling Zhang |
| 2025 | ICML | LADA: Scalable Label-Specific CLIP Adapter for Continual Learning. | Mao-Lin Luo, Zi-Hao Zhou, Tong Wei, Min-Ling Zhang |
| 2025 | ICML | Generalization Analysis for Controllable Learning. | Yifan Zhang, Xiao Zhang, Min-Ling Zhang |
| 2025 | ICML | Tight and Fast Bounds for Multi-Label Learning. | Yifan Zhang, Min-Ling Zhang |
| 2025 | ICML | Weakly-Supervised Contrastive Learning for Imprecise Class Labels. | Zi-Hao Zhou, Junjie Wang, Tong Wei, Min-Ling Zhang |
| 2025 | IJCAI | Wrapped Partial Label Dimensionality Reduction via Dependence Maximization. | Xiang-Ru Yu, Deng-Bao Wang, Min-Ling Zhang |
| 2024 | AAAI | EAT: Towards Long-Tailed Out-of-Distribution Detection. | Tong Wei, Bo-Lin Wang, Min-Ling Zhang |
| 2024 | AAAI | Disentangled Partial Label Learning. | Wei-Xuan Bao, Yong Rui, Min-Ling Zhang |
| 2024 | AAAI | Long-Tailed Partial Label Learning by Head Classifier and Tail Classifier Cooperation. | Yuheng Jia, Xiaorui Peng, Ran Wang, Min-Ling Zhang |
| 2024 | AAAI | Distilling Reliable Knowledge for Instance-Dependent Partial Label Learning. | Dong-Dong Wu, Deng-Bao Wang, Min-Ling Zhang |
| 2024 | CVPR | Efficient Model Stealing Defense with Noise Transition Matrix. | Dong-Dong Wu, Chilin Fu, Weichang Wu, Wenwen Xia, Xiaolu Zhang, Jun Zhou, Min-Ling Zhang |
| 2024 | ICDM | PROMIPL: A Probabilistic Generative Model for Multi-Instance Partial-Label Learning. | Yin-Fang Yang, Wei Tang, Min-Ling Zhang |
| 2024 | ICML | Learning Label Shift Correction for Test-Agnostic Long-Tailed Recognition. | Tong Wei, Zhen Mao, Zi-Hao Zhou, Yuanyu Wan, Min-Ling Zhang |
| 2024 | ICML | Binary Decomposition: A Problem Transformation Perspective for Open-Set Semi-Supervised Learning. | Jun-Yi Hang, Min-Ling Zhang |
| 2024 | ICML | Calibration Bottleneck: Over-compressed Representations are Less Calibratable. | Deng-Bao Wang, Min-Ling Zhang |
| 2024 | ICML | Generalization Analysis for Multi-Label Learning. | Yifan Zhang, Min-Ling Zhang |
| 2024 | IJCAI | Deep Multi-Dimensional Classification with Pairwise Dimension-Specific Features. | Teng Huang, Bin-Bin Jia, Min-Ling Zhang |
| 2024 | IJCAI | Learning Label-Specific Multiple Local Metrics for Multi-Label Classification. | Junxiang Mao, Jun-Yi Hang, Min-Ling Zhang |
| 2024 | IJCAI | Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning. | Wei Tang, Weijia Zhang, Min-Ling Zhang |
| 2024 | IJCAI | Unlearning from Weakly Supervised Learning. | Yi Tang, Yi Gao, Yonggang Luo, Jucheng Yang, Miao Xu, Min-Ling Zhang |
| 2024 | IJCAI | Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised Learning. | Bo Ye, Kai Gan, Tong Wei, Min-Ling Zhang |
| 2023 | AAAI | Partial-Label Regression. | Xin Cheng, Deng-Bao Wang, Lei Feng, Min-Ling Zhang, Bo An |
| 2023 | AAAI | Can Label-Specific Features Help Partial-Label Learning? | Ruo-Jing Dong, Jun-Yi Hang, Tong Wei, Min-Ling Zhang |
| 2023 | CVPR | On the Pitfall of Mixup for Uncertainty Calibration. | Deng-Bao Wang, Lanqing Li, Peilin Zhao, Pheng-Ann Heng, Min-Ling Zhang |
| 2023 | ICML | Nearly-tight Bounds for Deep Kernel Learning. | Yifan Zhang, Min-Ling Zhang |
| 2023 | IJCAI | Unbiased Risk Estimator to Multi-Labeled Complementary Label Learning. | Yi Gao, Miao Xu, Min-Ling Zhang |
| 2023 | IJCAI | Progressive Label Propagation for Semi-Supervised Multi-Dimensional Classification. | Teng Huang, Bin-Bin Jia, Min-Ling Zhang |
| 2023 | IJCAI | Stochastic Feature Averaging for Learning with Long-Tailed Noisy Labels. | Hao-Tian Li, Tong Wei, Hao Yang, Kun Hu, Chong Peng, Li-Bo Sun, Xun-Liang Cai, Min-Ling Zhang |
| 2023 | IJCAI | Label Specific Multi-Semantics Metric Learning for Multi-Label Classification: Global Consideration Helps. | Junxiang Mao, Wei Wang, Min-Ling Zhang |
| 2023 | KDD | Complementary Classifier Induced Partial Label Learning. | Yuheng Jia, Chongjie Si, Min-Ling Zhang |
| 2022 | AAAI | End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification. | Jun-Yi Hang, Min-Ling Zhang, Yanghe Feng, Xiaocheng Song |
| 2022 | ICML | Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification. | Jun-Yi Hang, Min-Ling Zhang |
| 2022 | ICML | Revisiting Consistency Regularization for Deep Partial Label Learning. | Dong-Dong Wu, Deng-Bao Wang, Min-Ling Zhang |
| 2022 | ICONIP | Partial Label Learning with Gradually Induced Error-Correction Output Codes. | Yu-Xuan Shi, Deng-Bao Wang, Min-Ling Zhang |
| 2022 | KDD | Submodular Feature Selection for Partial Label Learning. | Wei-Xuan Bao, Jun-Yi Hang, Min-Ling Zhang |
| 2022 | KDD | Partial Label Learning with Discrimination Augmentation. | Wei Wang, Min-Ling Zhang |
| 2022 | PAKDD | Prototypical Classifier for Robust Class-Imbalanced Learning. | Tong Wei, Jiang-Xin Shi, Yufeng Li, Min-Ling Zhang |
| 2021 | AAAI | Learning from Noisy Labels with Complementary Loss Functions. | Deng-Bao Wang, Yong Wen, Lujia Pan, Min-Ling Zhang |
| 2021 | AAAI | Exploiting Unlabeled Data via Partial Label Assignment for Multi-Class Semi-Supervised Learning. | Zhen-Ru Zhang, Qian-Wen Zhang, Yunbo Cao, Min-Ling Zhang |
| 2021 | ICML | Discriminative Complementary-Label Learning with Weighted Loss. | Yi Gao, Min-Ling Zhang |
| 2021 | ICML | Multi-Dimensional Classification via Sparse Label Encoding. | Bin-Bin Jia, Min-Ling Zhang |
| 2021 | IJCAI | BAMBOO: A Multi-instance Multi-label Approach Towards VDI User Logon Behavior Modeling. | Wen-Ping Fan, Yao Zhang, Qichen Hao, Xinya Wu, Min-Ling Zhang |
| 2021 | IJCAI | Learning from Complementary Labels via Partial-Output Consistency Regularization. | Deng-Bao Wang, Lei Feng, Min-Ling Zhang |
| 2021 | IJCAI | Correlation-Guided Representation for Multi-Label Text Classification. | Qian-Wen Zhang, Ximing Zhang, Zhao Yan, Ruifang Liu, Yunbo Cao, Min-Ling Zhang |
| 2021 | KDD | Partial Label Dimensionality Reduction via Confidence-Based Dependence Maximization. | Wei-Xuan Bao, Jun-Yi Hang, Min-Ling Zhang |
| 2021 | KDD | Tac-Valuer: Knowledge-based Stroke Evaluation in Table Tennis. | Jiachen Wang, Dazhen Deng, Xiao Xie, Xinhuan Shu, Yu-Xuan Huang, Le-Wen Cai, Hui Zhang, Min-Ling Zhang, Zhi-Hua Zhou, Yingcai Wu |
| 2020 | AAAI | Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation. | Ze-Sen Chen, Xuan Wu, Qing-Guo Chen, Yao Hu, Min-Ling Zhang |
| 2020 | AAAI | Maximum Margin Multi-Dimensional Classification. | Bin-Bin Jia, Min-Ling Zhang |
| 2020 | ICPR | Md-knn: An Instance-based Approach for Multi-Dimensional Classification. | Bin-Bin Jia, Min-Ling Zhang |
| 2020 | KDD | Feature-Induced Manifold Disambiguation for Multi-View Partial Multi-label Learning. | Jing-Han Wu, Xuan Wu, Qing-Guo Chen, Yao Hu, Min-Ling Zhang |
| 2019 | AAAI | Partial Multi-Label Learning via Credible Label Elicitation. | Jun-Peng Fang, Min-Ling Zhang |
| 2019 | AAAI | Multi-Dimensional Classification via kNN Feature Augmentation. | Bin-Bin Jia, Min-Ling Zhang |
| 2019 | AAAI | CAFE: Adaptive VDI Workload Prediction with Multi-Grained Features. | Yao Zhang, Wen-Ping Fan, Xuan Wu, Hua Chen, Bin-Yang Li, Min-Ling Zhang |
| 2019 | ACML | Multi-Label Learning with Regularization Enriched Label-Specific Features. | Ze-Sen Chen, Min-Ling Zhang |
| 2019 | IJCAI | Multi-View Multi-Label Learning with View-Specific Information Extraction. | Xuan Wu, Qing-Guo Chen, Yao Hu, Dengbao Wang, Xiaodong Chang, Xiaobo Wang, Min-Ling Zhang |
| 2019 | KDD | Adaptive Graph Guided Disambiguation for Partial Label Learning. | Deng-Bao Wang, Li Li, Min-Ling Zhang |
| 2019 | KDD | Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction. | Jing-Han Wu, Min-Ling Zhang |
| 2018 | AAAI | Feature-Induced Labeling Information Enrichment for Multi-Label Learning. | Qian-Wen Zhang, Yun Zhong, Min-Ling Zhang |
| 2018 | GECCO | A new R2 indicator for better hypervolume approximation. | Ke Shang, Hisao Ishibuchi, Min-Ling Zhang, Yiping Liu |
| 2018 | ICDM | Imbalanced Augmented Class Learning with Unlabeled Data by Label Confidence Propagation. | Si-Yu Ding, Xu-Ying Liu, Min-Ling Zhang |
| 2018 | IJCAI | Towards Enabling Binary Decomposition for Partial Label Learning. | Xuan Wu, Min-Ling Zhang |
| 2018 | KDD | Towards Mitigating the Class-Imbalance Problem for Partial Label Learning. | Jing Wang, Min-Ling Zhang |
| 2017 | AAAI | Confidence-Rated Discriminative Partial Label Learning. | Cai-Zhi Tang, Min-Ling Zhang |
| 2017 | DSAA | Multi-label Learning with Label-Specific Features via Clustering Ensemble. | Wang Zhan, Min-Ling Zhang |
| 2017 | IJCAI | Binary Linear Compression for Multi-label Classification. | Wen-Ji Zhou, Yang Yu, Min-Ling Zhang |
| 2017 | KDD | Inductive Semi-supervised Multi-Label Learning with Co-Training. | Wang Zhan, Min-Ling Zhang |
| 2016 | AAAI | Multi-Label Manifold Learning. | Peng Hou, Xin Geng, Min-Ling Zhang |
| 2016 | KDD | Partial Label Learning via Feature-Aware Disambiguation. | Min-Ling Zhang, Bin-Bin Zhou, Xu-Ying Liu |
| 2015 | ACML | Maximum Margin Partial Label Learning. | Fei Yu, Min-Ling Zhang |
| 2015 | ICDM | Leveraging Implicit Relative Labeling-Importance Information for Effective Multi-label Learning. | Yu-Kun Li, Min-Ling Zhang, Xin Geng |
| 2015 | IJCAI | Towards Class-Imbalance Aware Multi-Label Learning. | Min-Ling Zhang, Yu-Kun Li, Xu-Ying Liu |
| 2015 | IJCAI | Solving the Partial Label Learning Problem: An Instance-Based Approach. | Min-Ling Zhang, Fei Yu |
| 2014 | PRICAI | Enhancing Binary Relevance for Multi-label Learning with Controlled Label Correlations Exploitation. | Yu-Kun Li, Min-Ling Zhang |
| 2014 | SDM | Disambiguation-Free Partial Label Learning. | Min-Ling Zhang |
| 2013 | ACML | Multi-Label Classification with Unlabeled Data: An Inductive Approach. | Le Wu, Min-Ling Zhang |
| 2011 | IJCAI | LIFT: Multi-Label Learning with Label-Specific Features. | Min-Ling Zhang |
| 2010 | ICDM | Exploiting Unlabeled Data to Enhance Ensemble Diversity. | Min-Ling Zhang, Zhi-Hua Zhou |
| 2010 | ICTAI | A k-Nearest Neighbor Based Multi-Instance Multi-Label Learning Algorithm. | Min-Ling Zhang |
| 2010 | KDD | Multi-label learning by exploiting label dependency. | Min-Ling Zhang, Kun Zhang |
| 2008 | ICDM | M3MIML: A Maximum Margin Method for Multi-instance Multi-label Learning. | Min-Ling Zhang, Zhi-Hua Zhou |
| 2007 | AAAI | Multi-Label Learning by Instance Differentiation. | Min-Ling Zhang, Zhi-Hua Zhou |
| 2005 | GRC | A k-nearest neighbor based algorithm for multi-label classification. | Min-Ling Zhang, Zhi-Hua Zhou |
| 2003 | ICTAI | A Novel Bag Generator for Image Database Retrieval With Multi-Instance Learning Techniques. | Zhi-Hua Zhou, Min-Ling Zhang, Ke-Jia Chen |