Ensembles of Multi-instance Learners. - Zhi-Hua Zhou; Min-Ling Zhang (ECML, 2003)

A Novel Bag Generator for Image Database Retrieval With Multi-Instance Learning Techniques. - Zhi-Hua Zhou; Min-Ling Zhang; Ke-Jia Chen 001 (ICTAI, 2003)

Ensembles of Multi-Instance Neural Networks. - Min-Ling Zhang; Zhi-Hua Zhou (IIP, 2004)

Improve Multi-Instance Neural Networks through Feature Selection. - Min-Ling Zhang; Zhi-Hua Zhou (Neural Process. Lett., 2004)

A k-nearest neighbor based algorithm for multi-label classification. - Min-Ling Zhang; Zhi-Hua Zhou (GrC, 2005)

Multi-Instance Multi-Label Learning with Application to Scene Classification. - Zhi-Hua Zhou; Min-Ling Zhang (NIPS, 2006)

Adapting RBF Neural Networks to Multi-Instance Learning. - Min-Ling Zhang; Zhi-Hua Zhou (Neural Process. Lett., 2006)

Multi-Label Neural Networks with Applications to Functional Genomics and Text Categorization. - Min-Ling Zhang; Zhi-Hua Zhou (IEEE Trans. Knowl. Data Eng., 2006)

Multi-Label Learning by Instance Differentiation. - Min-Ling Zhang; Zhi-Hua Zhou (AAAI, 2007)

Solving multi-instance problems with classifier ensemble based on constructive clustering. - Zhi-Hua Zhou; Min-Ling Zhang (Knowl. Inf. Syst., 2007)

ML-KNN: A lazy learning approach to multi-label learning. - Min-Ling Zhang; Zhi-Hua Zhou (Pattern Recognit., 2007)

M3MIML: A Maximum Margin Method for Multi-instance Multi-label Learning. - Min-Ling Zhang; Zhi-Hua Zhou (ICDM, 2008)

MIML: A Framework for Learning with Ambiguous Objects - Zhi-Hua Zhou; Min-Ling Zhang; Sheng-Jun Huang; Yufeng Li 008 (CoRR, 2008)

Multi-instance clustering with applications to multi-instance prediction. - Min-Ling Zhang; Zhi-Hua Zhou (Appl. Intell., 2009)

Classifier Ensemble with Unlabeled Data - Min-Ling Zhang; Zhi-Hua Zhou (CoRR, 2009)

MIMLRBF: RBF neural networks for multi-instance multi-label learning. - Min-Ling Zhang; Zhijian Wang 002 (Neurocomputing, 2009)

Feature selection for multi-label naive Bayes classification. - Min-Ling Zhang; José María Peña Sánchez; Víctor Robles (Inf. Sci., 2009)

Ml-rbf : RBF Neural Networks for Multi-Label Learning. - Min-Ling Zhang (Neural Process. Lett., 2009)

A k-Nearest Neighbor Based Multi-Instance Multi-Label Learning Algorithm. - Min-Ling Zhang (ICTAI (2), 2010)

Multi-label learning by exploiting label dependency. - Min-Ling Zhang; Kun Zhang 001 (KDD, 2010)

LIFT: Multi-Label Learning with Label-Specific Features. - Min-Ling Zhang (IJCAI, 2011)

CoTrade: Confident Co-Training With Data Editing. - Min-Ling Zhang; Zhi-Hua Zhou (IEEE Trans. Syst. Man Cybern. Part B, 2011)

Multi-instance multi-label learning. - Zhi-Hua Zhou; Min-Ling Zhang; Sheng-Jun Huang; Yufeng Li 008 (Artif. Intell., 2012)

Introduction to the special issue on learning from multi-label data. - Grigorios Tsoumakas; Min-Ling Zhang; Zhi-Hua Zhou (Mach. Learn., 2012)

Multi-Label Classification with Unlabeled Data: An Inductive Approach. - Le Wu; Min-Ling Zhang (ACML, 2013)

Exploiting unlabeled data to enhance ensemble diversity. - Min-Ling Zhang; Zhi-Hua Zhou (Data Min. Knowl. Discov., 2013)

Enhancing Binary Relevance for Multi-label Learning with Controlled Label Correlations Exploitation. - Yu-Kun Li; Min-Ling Zhang (PRICAI, 2014)

Disambiguation-Free Partial Label Learning. - Min-Ling Zhang (SDM, 2014)

A Review on Multi-Label Learning Algorithms. - Min-Ling Zhang; Zhi-Hua Zhou (IEEE Trans. Knowl. Data Eng., 2014)

Leveraging Implicit Relative Labeling-Importance Information for Effective Multi-label Learning. - Yu-Kun Li; Min-Ling Zhang; Xin Geng 001 (ICDM, 2015)

Towards Class-Imbalance Aware Multi-Label Learning. - Min-Ling Zhang; Yu-Kun Li; Xu-Ying Liu (IJCAI, 2015)

Solving the Partial Label Learning Problem: An Instance-Based Approach. - Min-Ling Zhang; Fei Yu 010 (IJCAI, 2015)

Lift: Multi-Label Learning with Label-Specific Features. - Min-Ling Zhang; Lei Wu (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Multi-Label Manifold Learning. - Peng Hou; Xin Geng 001; Min-Ling Zhang (AAAI, 2016)

Partial Label Learning via Feature-Aware Disambiguation. - Min-Ling Zhang; Bin-Bin Zhou; Xu-Ying Liu (KDD, 2016)

Confidence-Rated Discriminative Partial Label Learning. - Cai-Zhi Tang; Min-Ling Zhang (AAAI, 2017)

Multi-label Learning with Label-Specific Features via Clustering Ensemble. - Wang Zhan; Min-Ling Zhang (DSAA, 2017)

Binary Linear Compression for Multi-label Classification. - Wen-Ji Zhou; Yang Yu 001; Min-Ling Zhang (IJCAI, 2017)

Inductive Semi-supervised Multi-Label Learning with Co-Training. - Wang Zhan; Min-Ling Zhang (KDD, 2017)

Maximum margin partial label learning. - Fei Yu 010; Min-Ling Zhang (Mach. Learn., 2017)

Disambiguation-Free Partial Label Learning. - Min-Ling Zhang; Fei Yu 010; Cai-Zhi Tang (IEEE Trans. Knowl. Data Eng., 2017)

Multi-label Learning. - Zhi-Hua Zhou; Min-Ling Zhang (Encyclopedia of Machine Learning and Data Mining, 2017)

Feature-Induced Labeling Information Enrichment for Multi-Label Learning. - Qian-Wen Zhang; Yun Zhong; Min-Ling Zhang (AAAI, 2018)

A new R2 indicator for better hypervolume approximation. - Ke Shang; Hisao Ishibuchi; Min-Ling Zhang; Yiping Liu (GECCO, 2018)

Imbalanced Augmented Class Learning with Unlabeled Data by Label Confidence Propagation. - Si-Yu Ding; Xu-Ying Liu; Min-Ling Zhang (ICDM, 2018)

Towards Enabling Binary Decomposition for Partial Label Learning. - Xuan Wu 003; Min-Ling Zhang (IJCAI, 2018)

Towards Mitigating the Class-Imbalance Problem for Partial Label Learning. - Jing Wang 113; Min-Ling Zhang (KDD, 2018)

Binary relevance for multi-label learning: an overview. - Min-Ling Zhang; Yu-Kun Li; Xu-Ying Liu; Xin Geng 001 (Frontiers Comput. Sci., 2018)

Weakly Supervised POS Tagging without Disambiguation. - Deyu Zhou; Zhikai Zhang; Min-Ling Zhang; Yulan He 001 (ACM Trans. Asian Low Resour. Lang. Inf. Process., 2018)

Multi-Dimensional Classification via kNN Feature Augmentation. - Bin-Bin Jia 001; Min-Ling Zhang (AAAI, 2019)

CAFE: Adaptive VDI Workload Prediction with Multi-Grained Features. - Yao Zhang 025; Wen-Ping Fan; Xuan Wu 003; Hua Chen; Bin-Yang Li; Min-Ling Zhang (AAAI, 2019)

Multi-Label Learning with Regularization Enriched Label-Specific Features. - Ze-Sen Chen; Min-Ling Zhang (ACML, 2019)

Multi-View Multi-Label Learning with View-Specific Information Extraction. - Xuan Wu 003; Qing-Guo Chen; Yao Hu; Dengbao Wang; Xiaodong Chang; Xiaobo Wang; Min-Ling Zhang (IJCAI, 2019)

Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction. - Jing-Han Wu; Min-Ling Zhang (KDD, 2019)

Transfer synthetic over-sampling for class-imbalance learning with limited minority class data. - Xu-Ying Liu; Sheng-Tao Wang; Min-Ling Zhang (Frontiers Comput. Sci., 2019)

Supervised representation learning for multi-label classification. - Ming Huang; Fuzhen Zhuang; Xiao Zhang 015; Xiang Ao 001; Zhengyu Niu; Min-Ling Zhang; Qing He 003 (Mach. Learn., 2019)

Neighborhood kinship preserving hashing for supervised learning. - Yan Cui 007; Jielin Jiang; Zuojin Hu; Xiaoyan Jiang; Wuxia Yan; Min-Ling Zhang (Signal Process. Image Commun., 2019)

Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation. - Ze-Sen Chen; Xuan Wu 003; Qing-Guo Chen; Yao Hu; Min-Ling Zhang (AAAI, 2020)

Md-knn: An Instance-based Approach for Multi-Dimensional Classification. - Bin-Bin Jia 001; Min-Ling Zhang (ICPR, 2020)

Feature-Induced Manifold Disambiguation for Multi-View Partial Multi-label Learning. - Jing-Han Wu; Xuan Wu 003; Qing-Guo Chen; Yao Hu; Min-Ling Zhang (KDD, 2020)

Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization. - Wei Wang 373; Min-Ling Zhang (NeurIPS, 2020)

Multi-dimensional classification via stacked dependency exploitation. - Bin-Bin Jia 001; Min-Ling Zhang (Sci. China Inf. Sci., 2020)

Preface. - Min-Ling Zhang; Yufeng Li 002; Qi Liu 003 (J. Comput. Sci. Technol., 2020)

Multi-dimensional classification via kNN feature augmentation. - Bin-Bin Jia 001; Min-Ling Zhang (Pattern Recognit., 2020)

Large-scale multi-label classification using unknown streaming images. - Yu Zhang 004; Yin Wang; Xu-Ying Liu; Siya Mi; Min-Ling Zhang (Pattern Recognit., 2020)

Learning from Noisy Labels with Complementary Loss Functions. - Deng-Bao Wang; Yong Wen; Lujia Pan; Min-Ling Zhang (AAAI, 2021)

Exploiting Unlabeled Data via Partial Label Assignment for Multi-Class Semi-Supervised Learning. - Zhen-Ru Zhang; Qian-Wen Zhang; Yunbo Cao; Min-Ling Zhang (AAAI, 2021)

Discriminative Complementary-Label Learning with Weighted Loss. - Yi Gao 003; Min-Ling Zhang (ICML, 2021)

Multi-Dimensional Classification via Sparse Label Encoding. - Bin-Bin Jia 001; Min-Ling Zhang (ICML, 2021)

BAMBOO: A Multi-instance Multi-label Approach Towards VDI User Logon Behavior Modeling. - Wen-Ping Fan; Yao Zhang 025; Qichen Hao; Xinya Wu; Min-Ling Zhang (IJCAI, 2021)

Learning from Complementary Labels via Partial-Output Consistency Regularization. - Deng-Bao Wang; Lei Feng 006; Min-Ling Zhang (IJCAI, 2021)

Correlation-Guided Representation for Multi-Label Text Classification. - Qian-Wen Zhang; Ximing Zhang; Zhao Yan; Ruifang Liu; Yunbo Cao; Min-Ling Zhang (IJCAI, 2021)

Partial Label Dimensionality Reduction via Confidence-Based Dependence Maximization. - Wei-Xuan Bao; Jun-Yi Hang; Min-Ling Zhang (KDD, 2021)

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 051; Min-Ling Zhang; Zhi-Hua Zhou; Yingcai Wu (KDD, 2021)

Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence. - Deng-Bao Wang; Lei Feng 006; Min-Ling Zhang (NeurIPS, 2021)

Instance-Dependent Partial Label Learning. - Ning Xu 009; Congyu Qiao; Xin Geng 001; Min-Ling Zhang (NeurIPS, 2021)

Learning from Noisy Labels via Dynamic Loss Thresholding. - Hao Yang 015; Youzhi Jin; Ziyin Li; Deng-Bao Wang; Lei Miao; Xin Geng 001; Min-Ling Zhang (CoRR, 2021)

Compositional metric learning for multi-label classification. - Yan-Ping Sun; Min-Ling Zhang (Frontiers Comput. Sci., 2021)

Preface. - Min-Ling Zhang; Sheng-Jun Huang; Mingsheng Long (J. Comput. Sci. Technol., 2021)

Partial Multi-Label Learning via Credible Label Elicitation. - Min-Ling Zhang; Jun-Peng Fang (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Leveraging Implicit Relative Labeling-Importance Information for Effective Multi-Label Learning. - Min-Ling Zhang; Qian-Wen Zhang; Jun-Peng Fang; Yu-Kun Li; Xin Geng 001 (IEEE Trans. Knowl. Data Eng., 2021)

End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification. - Jun-Yi Hang; Min-Ling Zhang; Yanghe Feng; Xiaocheng Song (AAAI, 2022)

Revisiting Consistency Regularization for Deep Partial Label Learning. - Dong-Dong Wu; Deng-Bao Wang; Min-Ling Zhang (ICML, 2022)

Partial Label Learning with Gradually Induced Error-Correction Output Codes. - Yu-Xuan Shi; Deng-Bao Wang; Min-Ling Zhang (ICONIP (1), 2022)

Submodular Feature Selection for Partial Label Learning. - Wei-Xuan Bao; Jun-Yi Hang; Min-Ling Zhang (KDD, 2022)

Partial Label Learning with Discrimination Augmentation. - Wei Wang 373; Min-Ling Zhang (KDD, 2022)

(Re-)connecting with Nature in Urban Life: Engaging with Wildlife via AI-powered Wearables. - Zhuying Li; Si Cheng; Wei Wang 133; Min-Ling Zhang (MobileHCI (Adjunct), 2022)

Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization. - Weijia Zhang 001; Xuanhui Zhang; Hanwen Deng; Min-Ling Zhang (NeurIPS, 2022)

One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement. - Ning Xu 009; Congyu Qiao; Jiaqi Lv; Xin Geng 001; Min-Ling Zhang (NeurIPS, 2022)

Prototypical Classifier for Robust Class-Imbalanced Learning. - Tong Wei 001; Jiang-Xin Shi; Yufeng Li 008; Min-Ling Zhang (PAKDD (2), 2022)

Towards Learning Causal Representations from Multi-Instance Bags. - Weijia Zhang 001; Xuanhui Zhang; Hanwen Deng; Min-Ling Zhang (CoRR, 2022)

A Survey on Extreme Multi-label Learning. - Tong Wei 001; Zhen Mao; Jiang-Xin Shi; Yufeng Li 008; Min-Ling Zhang (CoRR, 2022)

Stable Label-Specific Features Generation for Multi-Label Learning via Mixture-Based Clustering Ensemble. - Yi-Bo Wang; Jun-Yi Hang; Min-Ling Zhang (IEEE CAA J. Autom. Sinica, 2022)

Multi-dimensional Classification via Selective Feature Augmentation. - Bin-Bin Jia 001; Min-Ling Zhang (Int. J. Autom. Comput., 2022)

Preface. - Min-Ling Zhang; Xiu-Shen Wei; Gao Huang 001 (J. Comput. Sci. Technol., 2022)

Collaborative Learning of Label Semantics and Deep Label-Specific Features for Multi-Label Classification. - Jun-Yi Hang; Min-Ling Zhang (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Adaptive Graph Guided Disambiguation for Partial Label Learning. - Deng-Bao Wang; Min-Ling Zhang; Li Li 006 (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Multi-Label Classification With Label-Specific Feature Generation: A Wrapped Approach. - Ze-Bang Yu; Min-Ling Zhang (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Decomposition-Based Classifier Chains for Multi-Dimensional Classification. - Bin-Bin Jia 001; Min-Ling Zhang (IEEE Trans. Artif. Intell., 2022)

Towards Class-Imbalance Aware Multi-Label Learning. - Min-Ling Zhang; Yu-Kun Li; Hao Yang 015; Xu-Ying Liu (IEEE Trans. Cybern., 2022)

CAFE and SOUP: Toward Adaptive VDI Workload Prediction. - Yao Zhang 025; Wenping Fan; Qichen Hao; Xinya Wu; Min-Ling Zhang (ACM Trans. Intell. Syst. Technol., 2022)

BiLabel-Specific Features for Multi-Label Classification. - Min-Ling Zhang; Jun-Peng Fang; Yi-Bo Wang (ACM Trans. Knowl. Discov. Data, 2022)

Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction. - Min-Ling Zhang; Jing-Han Wu; Wei-Xuan Bao (ACM Trans. Knowl. Discov. Data, 2022)

Maximum Margin Multi-Dimensional Classification. - Bin-Bin Jia 001; Min-Ling Zhang (IEEE Trans. Neural Networks Learn. Syst., 2022)

Partial-Label Regression. - Xin Cheng 007; Deng-Bao Wang; Lei Feng 006; Min-Ling Zhang; Bo An 001 (AAAI, 2023)

Can Label-Specific Features Help Partial-Label Learning? - Ruo-Jing Dong; Jun-Yi Hang; Tong Wei 001; Min-Ling Zhang (AAAI, 2023)

On the Pitfall of Mixup for Uncertainty Calibration. - Deng-Bao Wang; Lanqing Li; Peilin Zhao; Pheng-Ann Heng; Min-Ling Zhang (CVPR, 2023)

Nearly-tight Bounds for Deep Kernel Learning. - Yifan Zhang; Min-Ling Zhang (ICML, 2023)

Unbiased Risk Estimator to Multi-Labeled Complementary Label Learning. - Yi Gao 003; Miao Xu 001; Min-Ling Zhang (IJCAI, 2023)

Progressive Label Propagation for Semi-Supervised Multi-Dimensional Classification. - Teng Huang 003; Bin-Bin Jia 001; Min-Ling Zhang (IJCAI, 2023)

Stochastic Feature Averaging for Learning with Long-Tailed Noisy Labels. - Hao-Tian Li; Tong Wei 001; Hao Yang 015; Kun Hu; Chong Peng; Li-Bo Sun; Xun-Liang Cai; Min-Ling Zhang (IJCAI, 2023)

Label Specific Multi-Semantics Metric Learning for Multi-Label Classification: Global Consideration Helps. - Junxiang Mao; Wei Wang 373; Min-Ling Zhang (IJCAI, 2023)

Complementary Classifier Induced Partial Label Learning. - Yuheng Jia; Chongjie Si; Min-Ling Zhang (KDD, 2023)

Partial Multi-Label Learning with Probabilistic Graphical Disambiguation. - Jun-Yi Hang; Min-Ling Zhang (NeurIPS, 2023)

Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning. - Wei Tang 017; Weijia Zhang 001; Min-Ling Zhang (NeurIPS, 2023)

Binary Classification with Confidence Difference. - Wei Wang 373; Lei Feng 006; Yuchen Jiang; Gang Niu 001; Min-Ling Zhang; Masashi Sugiyama (NeurIPS, 2023)

Transformer-based Multi-Instance Learning for Weakly Supervised Object Detection. - Zhaofei Wang; Weijia Zhang 001; Min-Ling Zhang (CoRR, 2023)

Rethinking the Value of Labels for Instance-Dependent Label Noise Learning. - Hanwen Deng; Weijia Zhang 001; Min-Ling Zhang (CoRR, 2023)

Robust Representation Learning for Unreliable Partial Label Learning. - Yu Shi; Dong-Dong Wu; Xin Geng 001; Min-Ling Zhang (CoRR, 2023)

Multi-Dimensional Classification via Decomposed Label Encoding. - Bin-Bin Jia 001; Min-Ling Zhang (IEEE Trans. Knowl. Data Eng., 2023)

Variational Label Enhancement. - Ning Xu 009; Jun Shu; RenYi Zheng; Xin Geng 001; Deyu Meng; Min-Ling Zhang (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Multi-dimensional multi-label classification: Towards encompassing heterogeneous label spaces and multi-label annotations. - Bin-Bin Jia 001; Min-Ling Zhang (Pattern Recognit., 2023)

Towards Enabling Binary Decomposition for Partial Multi-Label Learning. - Bing-Qing Liu; Bin-Bin Jia 001; Min-Ling Zhang (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Learning label-specific features for decomposition-based multi-class classification. - Bin-Bin Jia 001; Jun-Ying Liu 001; Jun-Yi Hang; Min-Ling Zhang (Frontiers Comput. Sci., 2023)

EAT: Towards Long-Tailed Out-of-Distribution Detection. - Tong Wei 001; Bo-Lin Wang; Min-Ling Zhang (AAAI, 2024)

Disentangled Partial Label Learning. - Wei-Xuan Bao; Yong Rui; Min-Ling Zhang (AAAI, 2024)

Long-Tailed Partial Label Learning by Head Classifier and Tail Classifier Cooperation. - Yuheng Jia; Xiaorui Peng; Ran Wang 001; Min-Ling Zhang (AAAI, 2024)

Distilling Reliable Knowledge for Instance-Dependent Partial Label Learning. - Dong-Dong Wu; Deng-Bao Wang; Min-Ling Zhang (AAAI, 2024)

Efficient Model Stealing Defense with Noise Transition Matrix. - Dong-Dong Wu; Chilin Fu; Weichang Wu; Wenwen Xia; Xiaolu Zhang; Jun Zhou 011; Min-Ling Zhang (CVPR, 2024)

PROMIPL: A Probabilistic Generative Model for Multi-Instance Partial-Label Learning. - Yin-Fang Yang; Wei Tang 017; Min-Ling Zhang (ICDM, 2024)

Proposal Feature Learning Using Proposal Relations for Weakly Supervised Object Detection. - Zhaofei Wang; Weijia Zhang 001; Min-Ling Zhang (ICME, 2024)

Learning Label Shift Correction for Test-Agnostic Long-Tailed Recognition. - Tong Wei 001; Zhen Mao; Zi-Hao Zhou; Yuanyu Wan; Min-Ling Zhang (ICML, 2024)

Binary Decomposition: A Problem Transformation Perspective for Open-Set Semi-Supervised Learning. - Jun-Yi Hang; Min-Ling Zhang (ICML, 2024)

Calibration Bottleneck: Over-compressed Representations are Less Calibratable. - Deng-Bao Wang; Min-Ling Zhang (ICML, 2024)

Generalization Analysis for Multi-Label Learning. - Yifan Zhang; Min-Ling Zhang (ICML, 2024)

Deep Multi-Dimensional Classification with Pairwise Dimension-Specific Features. - Teng Huang 003; Bin-Bin Jia 001; Min-Ling Zhang (IJCAI, 2024)

Learning Label-Specific Multiple Local Metrics for Multi-Label Classification. - Junxiang Mao; Jun-Yi Hang; Min-Ling Zhang (IJCAI, 2024)

Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning. - Wei Tang 017; Weijia Zhang 001; Min-Ling Zhang (IJCAI, 2024)

Unlearning from Weakly Supervised Learning. - Yi Tang; Yi Gao 003; Yonggang Luo; Jucheng Yang; Miao Xu 001; Min-Ling Zhang (IJCAI, 2024)

Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised Learning. - Bo Ye; Kai Gan; Tong Wei 001; Min-Ling Zhang (IJCAI, 2024)

Improved Weighted Tensor Schatten p-Norm for Fast Multi-view Graph Clustering. - Yinghui Sun; Xingfeng Li 004; Quansen Sun; Min-Ling Zhang; Zhenwen Ren (ACM Multimedia, 2024)

What Makes Partial-Label Learning Algorithms Effective? - Jiaqi Lv; Yangfan Liu; Shiyu Xia; Ning Xu 009; Miao Xu 001; Gang Niu 001; Min-Ling Zhang; Masashi Sugiyama; Xin Geng 001 (NeurIPS, 2024)

Multi-Instance Partial-Label Learning with Margin Adjustment. - Wei Tang 017; Yin-Fang Yang; Zhaofei Wang; Weijia Zhang 001; Min-Ling Zhang (NeurIPS, 2024)

Multi-Label Open Set Recognition. - Yibo Wang; Jun-Yi Hang; Min-Ling Zhang (NeurIPS, 2024)

Vision-Language Models are Strong Noisy Label Detectors. - Tong Wei 001; Hao-Tian Li; Chun-Shu Li; Jiang-Xin Shi; Yufeng Li 008; Min-Ling Zhang (NeurIPS, 2024)

Generalization Analysis for Label-Specific Representation Learning. - Yi-Fan Zhang; Min-Ling Zhang (NeurIPS, 2024)

Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition. - Zi-Hao Zhou; Siyuan Fang; Zi-Jing Zhou; Tong Wei 001; Yuanyu Wan; Min-Ling Zhang (NeurIPS, 2024)

Multi-instance partial-label learning: towards exploiting dual inexact supervision. - Wei Tang 017; Weijia Zhang 001; Min-Ling Zhang (Sci. China Inf. Sci., 2024)

Boosting Consistency in Dual Training for Long-Tailed Semi-Supervised Learning. - Kai Gan; Tong Wei 001; Min-Ling Zhang (CoRR, 2024)

Attention Is Not What You Need: Revisiting Multi-Instance Learning for Whole Slide Image Classification. - Xin Liu 088; Weijia Zhang 001; Min-Ling Zhang (CoRR, 2024)

HACSurv: A Hierarchical Copula-based Approach for Survival Analysis with Dependent Competing Risks. - Xin Liu 088; Weijia Zhang 001; Min-Ling Zhang (CoRR, 2024)

Multi-Label Classification With High-Rank and High-Order Label Correlations. - Chongjie Si; Yuheng Jia; Ran Wang 001; Min-Ling Zhang; Yanghe Feng; Chongxiao Qu (IEEE Trans. Knowl. Data Eng., 2024)

Dimensionality Reduction for Partial Label Learning: A Unified and Adaptive Approach. - Xiang-Ru Yu 001; Deng-Bao Wang; Min-Ling Zhang (IEEE Trans. Knowl. Data Eng., 2024)

Temporal segment dropout for human action video recognition. - Yu Zhang 004; Zhengjie Chen; Tianyu Xu; Junjie Zhao; Siya Mi; Xin Geng 001; Min-Ling Zhang (Pattern Recognit., 2024)

Partial label learning with emerging new labels. - Xiang-Ru Yu 001; Deng-Bao Wang; Min-Ling Zhang (Mach. Learn., 2024)

Multiple-instance Learning from Triplet Comparison Bags. - Senlin Shu; Deng-Bao Wang; Suqin Yuan; Hongxin Wei; Jiuchuan Jiang; Lei Feng 006; Min-Ling Zhang (ACM Trans. Knowl. Discov. Data, 2024)

Student Loss: Towards the Probability Assumption in Inaccurate Supervision. - Shuo Zhang 030; Jianqing Li 002; Hamido Fujita; Yu-Wen Li 002; Deng-Bao Wang; Tingting Zhu 001; Min-Ling Zhang; Chengyu Liu 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Towards exploiting linear regression for multi-class/multi-label classification: an empirical analysis. - Bin-Bin Jia 001; Jun-Ying Liu 001; Min-Ling Zhang (Int. J. Mach. Learn. Cybern., 2024)

Label-Specific Time-Frequency Energy-Based Neural Network for Instrument Recognition. - Jian Zhang 019; Tong Wei 001; Min-Ling Zhang (IEEE Trans. Cybern., 2024)

Learning From Noisy Labels via Dynamic Loss Thresholding. - Hao Yang 015; Youzhi Jin; Ziyin Li; Deng-Bao Wang; Xin Geng 001; Min-Ling Zhang (IEEE Trans. Knowl. Data Eng., 2024)

Complementary to Multiple Labels: A Correlation-Aware Correction Approach. - Yi Gao 003; Miao Xu 001; Min-Ling Zhang (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Variational Label Enhancement for Instance-Dependent Partial Label Learning. - Ning Xu 009; Congyu Qiao; Yuchen Zhao; Xin Geng 001; Min-Ling Zhang (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Learnware Specification via Label-Aware Neural Embedding. - Wei Chen 141; Junxiang Mao; Min-Ling Zhang (AAAI, 2025)

Implicit Relative Labeling-Importance Aware Multi-Label Metric Learning. - Junxiang Mao; Yong Rui; Min-Ling Zhang (AAAI, 2025)

Partial Label Causal Representation Learning for Instance-Dependent Supervision and Domain Generalization. - Yizhi Wang; Weijia Zhang 001; Min-Ling Zhang (AAAI, 2025)

Fast Multi-Instance Partial-Label Learning. - Yin-Fang Yang; Wei Tang 017; Min-Ling Zhang (AAAI, 2025)

Evolutionary Classifier Chain for Multi-Dimensional Classification. - Yu-Yang Zhang; Bin-Bin Jia 001; Min-Ling Zhang (AAAI, 2025)

HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks. - Xin Liu; Weijia Zhang; Min-Ling Zhang (AISTATS, 2025)

ZzzMate: A Self-Conscious Emotion-Aware Chatbot for Sleep Intervention. - Xiao Tang; Zhuying Li; Xin Sun 016; Xuhai Xu; Min-Ling Zhang (CHI Extended Abstracts, 2025)

Realistic Evaluation of Deep Partial-Label Learning Algorithms. - Wei Wang 373; Dong-Dong Wu; Jindong Wang 001; Gang Niu 001; Min-Ling Zhang; Masashi Sugiyama (ICLR, 2025)

Semi-Supervised CLIP Adaptation by Enforcing Semantic and Trapezoidal Consistency. - Kai Gan; Bo Ye; Min-Ling Zhang; Tong Wei 001 (ICLR, 2025)

Noise Separation guided Candidate Label Reconstruction for Noisy Partial Label Learning. - Xiaorui Peng; Yuheng Jia; Fuchao Yang; Ran Wang 001; Min-Ling Zhang (ICLR, 2025)

Partial multi-label learning via label-specific feature corrections. - Jun-Yi Hang; Min-Ling Zhang (Sci. China Inf. Sci., 2025)

Instance-dependent label noise learning via separating style from content. - Han-Wen Deng; Weijia Zhang 001; Min-Ling Zhang (Pattern Recognit. Lett., 2025)

Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification. - Jun-Yi Hang; Min-Ling Zhang (ACM Trans. Knowl. Discov. Data, 2025)

Adaptive VDI Session Placement via User Logoff Prediction. - Wenping Fan; Puhui Meng; Yu Tian; Min-Ling Zhang; Yao Zhang 025 (Mach. Intell. Res., 2025)

Weakly-Supervised Contrastive Learning for Imprecise Class Labels. - Zi-Hao Zhou; Jun-Jie Wang; Tong Wei; Min-Ling Zhang (CoRR, 2025)

LADA: Scalable Label-Specific CLIP Adapter for Continual Learning. - Mao-Lin Luo; Zi-Hao Zhou; Tong Wei; Min-Ling Zhang (CoRR, 2025)

Tuning the Right Foundation Models is What you Need for Partial Label Learning. - Kuang He; Wei Tang; Tong Wei; Min-Ling Zhang (CoRR, 2025)

Instance-Specific Loss-Weighted Decoding for Decomposition-Based Multiclass Classification. - Bin-Bin Jia 001; Jun-Ying Liu 001; Min-Ling Zhang (IEEE Trans. Neural Networks Learn. Syst., 2025)

Don't Overthink It: A Survey of Efficient R1-style Large Reasoning Models. - Linan Yue; Yichao Du; Yizhi Wang; Weibo Gao; Fangzhou Yao; Li Wang 014; Ye Liu 011; Ziyu Xu; Qi Liu 003; Shimin Di; Min-Ling Zhang (CoRR, 2025)

What Makes "Good" Distractors for Object Hallucination Evaluation in Large Vision-Language Models? - Ming-Kun Xie; Jiahao Xiao; Gang Niu 001; Lei Feng 006; Zhiqiang Kou; Min-Ling Zhang; Masashi Sugiyama (CoRR, 2025)

Multi-Label Learning With Multiple Complementary Labels. - Yi Gao 003; Jing-Yi Zhu; Miao Xu 001; Min-Ling Zhang (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

Pairwise statistical comparisons of multiple algorithms. - Bin-Bin Jia 001; Jun-Ying Liu 001; Min-Ling Zhang (Frontiers Comput. Sci., 2025)

Partial label learning with semi-supervised clustering disambiguation. - Jun-Ying Liu 001; Jian-Ping Sun; Ya-Hong Zhao; Bin-Bin Jia 001; Min-Ling Zhang (Pattern Recognit., 2026)