Partial Multi-Label Learning. - Ming-Kun Xie; Sheng-Jun Huang (AAAI, 2018)

Active Feature Acquisition with Supervised Matrix Completion. - Sheng-Jun Huang; Miao Xu 001; Ming-Kun Xie; Masashi Sugiyama; Gang Niu 001; Songcan Chen (KDD, 2018)

Learning Class-Conditional GANs with Active Sampling. - Ming-Kun Xie; Sheng-Jun Huang (KDD, 2019)

Semi-Supervised Partial Multi-Label Learning. - Ming-Kun Xie; Sheng-Jun Huang (ICDM, 2020)

Partial Multi-Label Learning with Meta Disambiguation. - Ming-Kun Xie; Feng Sun; Sheng-Jun Huang (KDD, 2021)

Multi-Label Learning with Pairwise Relevance Ordering. - Ming-Kun Xie; Sheng-Jun Huang (NeurIPS, 2021)

Label-Aware Global Consistency for Multi-Label Learning with Single Positive Labels. - Ming-Kun Xie; Jiahao Xiao; Sheng-Jun Huang (NeurIPS, 2022)

Meta Objective Guided Disambiguation for Partial Label Learning. - Bo-Shi Zou; Ming-Kun Xie; Sheng-Jun Huang (CoRR, 2022)

Noise-Robust Bidirectional Learning with Dynamic Sample Reweighting. - Chen-Chen Zong; Zheng-Tao Cao; Hong-Tao Guo; Yun Du; Ming-Kun Xie; Shao-Yuan Li; Sheng-Jun Huang (CoRR, 2022)

Partial Multi-Label Learning With Noisy Label Identification. - Ming-Kun Xie; Sheng-Jun Huang (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Multi-Label Knowledge Distillation. - Penghui Yang 001; Ming-Kun Xie; Chen-Chen Zong; Lei Feng 006; Gang Niu 001; Masashi Sugiyama; Sheng-Jun Huang (ICCV, 2023)

Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning. - Ming-Kun Xie; Jiahao Xiao; Hao-Zhe Liu; Gang Niu 001; Masashi Sugiyama; Sheng-Jun Huang (NeurIPS, 2023)

Unlocking the Power of Open Set : A New Perspective for Open-set Noisy Label Learning. - Wenhai Wan; Xinrui Wang 003; Ming-Kun Xie; Shengjun Huang; Songcan Chen; Shaoyuan Li (CoRR, 2023)

CCMN: A General Framework for Learning With Class-Conditional Multi-Label Noise. - Ming-Kun Xie; Sheng-Jun Huang (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Unlocking the Power of Open Set: A New Perspective for Open-Set Noisy Label Learning. - Wenhai Wan; Xinrui Wang 003; Ming-Kun Xie; Shao-Yuan Li; Sheng-Jun Huang; Songcan Chen (AAAI, 2024)

Dirichlet-Based Prediction Calibration for Learning with Noisy Labels. - Chen-Chen Zong; Ye-Wen Wang; Ming-Kun Xie; Sheng-Jun Huang (AAAI, 2024)

Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-supervised Multi-label Learning. - Jiahao Xiao; Ming-Kun Xie; Heng-Bo Fan; Gang Niu 001; Masashi Sugiyama; Sheng-Jun Huang (ECCV (52), 2024)

Dirichlet-Based Coarse-to-Fine Example Selection For Open-Set Annotation. - Ye-Wen Wang; Chen-Chen Zong; Ming-Kun Xie; Sheng-Jun Huang (ICME, 2024)

Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. - Ming-Kun Xie; Jiahao Xiao; Pei Peng 005; Gang Niu 001; Masashi Sugiyama; Sheng-Jun Huang (ICML, 2024)

Asymmetric Beta Loss for Evidence-Based Safe Semi-Supervised Multi-Label Learning. - Hao-Zhe Liu; Ming-Kun Xie; Chen-Chen Zong; Sheng-Jun Huang (KDD, 2024)

Optimistic Critic Reconstruction and Constrained Fine-Tuning for General Offline-to-Online RL. - Qin-Wen Luo; Ming-Kun Xie; Ye-Wen Wang; Sheng-Jun Huang (NeurIPS, 2024)

Context-Based Semantic-Aware Alignment for Semi-Supervised Multi-Label Learning. - Heng-Bo Fan; Ming-Kun Xie; Jiahao Xiao; Sheng-Jun Huang (CoRR, 2024)

Robust AUC maximization for classification with pairwise confidence comparisons. - Haochen Shi; Ming-Kun Xie; Shengjun Huang (Frontiers Comput. Sci., 2024)

A Deep Model for Partial Multi-label Image Classification with Curriculum-based Disambiguation. - Feng Sun; Ming-Kun Xie; Sheng-Jun Huang (Mach. Intell. Res., 2024)

Sequential Cooperative Distillation for Imbalanced Multi-Task Learning. - Quan Feng; Jia-Yu Yao; Ming-Kun Xie; Sheng-Jun Huang; Songcan Chen (J. Comput. Sci. Technol., 2024)

UNM: A Universal Approach for Noisy Multi-Label Learning. - Jia-Yao Chen; Shao-Yuan Li; Sheng-Jun Huang; Songcan Chen; Lei Wang 226; Ming-Kun Xie (IEEE Trans. Knowl. Data Eng., 2024)

Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning. - Lei-Lei Ma; Shuo Xu; Ming-Kun Xie; Lei Wang 095; Dengdi Sun; Haifeng Zhao 001 (CVPR, 2025)

Interactive Large Language Models for Reliable Answering under Incomplete Context. - Jing-Cheng Pang; Heng-Bo Fan; Pengyuan Wang; Jiahao Xiao; Nan Tang; Si-Hang Yang; Chengxing Jia; Ming-Kun Xie; Xiang Chen 016; Sheng-Jun Huang; Yang Yu 001 (Trans. Mach. Learn. Res., 2025)

Label Distribution Learning with Biased Annotations by Learning Multi-Label Representation. - Zhiqiang Kou; Si Qin; Hailin Wang; Ming-Kun Xie; Shuo Chen 003; Yuheng Jia; Tongliang Liu; Masashi Sugiyama; Xin Geng 001 (CoRR, 2025)

Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning. - Leilei Ma 002; Shuo Xu; Ming-Kun Xie; Lei Wang 095; Dengdi Sun; Haifeng Zhao 001 (CoRR, 2025)

Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RL. - Qin-Wen Luo; Ming-Kun Xie; Ye-Wen Wang; Sheng-Jun Huang (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)

A Unified Open Adapter for Open-World Noisy Label Learning: Data-Centric and Learning-Based Insights. - Chen-Chen Zong; Penghui Yang 001; Ming-Kun Xie; Sheng-Jun Huang (IEEE Trans. Circuits Syst. Video Technol., 2025)