← Authors

Min-ling Zhang

79 papers · 2006–2026 · 8 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer πŸƒ Academic Marathon (19) 🌈 Renaissance Researcher (6) 🌍 Conference Polyglot (8) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (10) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (20) 🌟 Keyword Trendsetter Combo (3) 🏠 Conference Loyalist (22) 🀝 Dynamic Duo (10) πŸ”¬ Deep Specialist (12) πŸ† Keyword Champion (2) πŸ† Grand Slam 🌱 Topic Pioneer πŸ”₯ Unstoppable (9) πŸ—ƒοΈ Keyword Collector (66) πŸ“ˆ Trend Setter ❓ The Questioner (2) πŸš€ Conference Pioneer ⚑ Prolific Year (11) πŸ’Ž Century Club (76)

Conferences

AAAI (22) IJCAI (18) ICML (15) NIPS (15) ACML (3) ICLR (3) CVPR (2) AISTATS (1)

Top co-authors

Tong Wei (10) Deng-Bao Wang (9) Bin-Bin Jia (7) Jun-Yi Hang (7) Weijia Zhang (6) Jun-Xiang Mao (5) Yong Rui (4) Lei Feng (4) Zi-Hao Zhou (4) Wei Wang (4)

Keywords

partial label learning (14) multi-label classification (13) representation learning (9) multi-label learning (8) semi-supervised learning (8) weakly supervised learning (8) multi-instance learning (7) label noise (6) partial-label learning (6) label disambiguation (6) multi-dimensional classification (6) label correlation (5) variational inference (4) multi-class classification (4) alternating optimization (4) candidate label (4) label-specific feature (4) metric learning (3) complementary label learning (3) label propagation (3)

Papers

Classifier-induced Reciprocal Points for Multi-label Open-set Recognition AAAI 2026
APVR: Hour-Level Long Video Understanding with Adaptive Pivot Visual Information Retrieval AAAI 2026
Collaborative Dual Representations for Semi-Supervised Partial Label Learning AAAI 2026
Realistic Evaluation of Deep Partial-Label Learning Algorithms ICLR 2025
Weakly-Supervised Contrastive Learning for Imprecise Class Labels ICML 2025
Semi-Supervised CLIP Adaptation by Enforcing Semantic and Trapezoidal Consistency ICLR 2025
Noise Separation guided Candidate Label Reconstruction for Noisy Partial Label Learning ICLR 2025
Generalization Analysis for Controllable Learning ICML 2025
Tight and Fast Bounds for Multi-Label Learning ICML 2025
LADA: Scalable Label-Specific CLIP Adapter for Continual Learning ICML 2025
Wrapped Partial Label Dimensionality Reduction via Dependence Maximization IJCAI 2025
Learnware Specification via Label-Aware Neural Embedding AAAI 2025
Implicit Relative Labeling-Importance Aware Multi-Label Metric Learning AAAI 2025
Partial Label Causal Representation Learning for Instance-Dependent Supervision and Domain Generalization AAAI 2025
Fast Multi-Instance Partial-Label Learning AAAI 2025
Evolutionary Classifier Chain for Multi-Dimensional Classification AAAI 2025
Towards Escaping from Class Dependency Modeling for Multi-Dimensional Classification ICML 2025
Learnware Specification via Dual Alignment ICML 2025
HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks AISTATS 2025
Efficient Model Stealing Defense with Noise Transition Matrix CVPR 2024
EAT: Towards Long-Tailed Out-of-Distribution Detection AAAI 2024
Distilling Reliable Knowledge for Instance-Dependent Partial Label Learning AAAI 2024
Learning Label Shift Correction for Test-Agnostic Long-Tailed Recognition ICML 2024
Generalization Analysis for Multi-Label Learning ICML 2024
Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised Learning IJCAI 2024
Disentangled Partial Label Learning AAAI 2024
Long-Tailed Partial Label Learning by Head Classifier and Tail Classifier Cooperation AAAI 2024
Binary Decomposition: A Problem Transformation Perspective for Open-Set Semi-Supervised Learning ICML 2024
Calibration Bottleneck: Over-compressed Representations are Less Calibratable ICML 2024
Multi-Label Open Set Recognition NIPS 2024
Multi-Instance Partial-Label Learning with Margin Adjustment NIPS 2024
Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition NIPS 2024
Vision-Language Models are Strong Noisy Label Detectors NIPS 2024
What Makes Partial-Label Learning Algorithms Effective? NIPS 2024
Generalization Analysis for Label-Specific Representation Learning NIPS 2024
Unlearning from Weakly Supervised Learning IJCAI 2024
Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning IJCAI 2024
Learning Label-Specific Multiple Local Metrics for Multi-Label Classification IJCAI 2024
Deep Multi-Dimensional Classification with Pairwise Dimension-Specific Features IJCAI 2024
Label Specific Multi-Semantics Metric Learning for Multi-Label Classification: Global Consideration Helps IJCAI 2023
Partial Multi-Label Learning with Probabilistic Graphical Disambiguation NIPS 2023
Binary Classification with Confidence Difference NIPS 2023
Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning NIPS 2023
Partial-Label Regression AAAI 2023
Can Label-Specific Features Help Partial-Label Learning? AAAI 2023
On the Pitfall of Mixup for Uncertainty Calibration CVPR 2023
Nearly-tight Bounds for Deep Kernel Learning ICML 2023
Unbiased Risk Estimator to Multi-Labeled Complementary Label Learning IJCAI 2023
Progressive Label Propagation for Semi-Supervised Multi-Dimensional Classification IJCAI 2023
Stochastic Feature Averaging for Learning with Long-Tailed Noisy Labels IJCAI 2023
Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification ICML 2022
One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement NIPS 2022
Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization NIPS 2022
Revisiting Consistency Regularization for Deep Partial Label Learning ICML 2022
End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification AAAI 2022
Multi-Dimensional Classification via Sparse Label Encoding ICML 2021
Exploiting Unlabeled Data via Partial Label Assignment for Multi-Class Semi-Supervised Learning AAAI 2021
Learning from Noisy Labels with Complementary Loss Functions AAAI 2021
BAMBOO: A Multi-instance Multi-label Approach Towards VDI User Logon Behavior Modeling IJCAI 2021
Learning from Complementary Labels via Partial-Output Consistency Regularization IJCAI 2021
Correlation-Guided Representation for Multi-Label Text Classification IJCAI 2021
Instance-Dependent Partial Label Learning NIPS 2021
Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence NIPS 2021
Discriminative Complementary-Label Learning with Weighted Loss ICML 2021
Maximum Margin Multi-Dimensional Classification AAAI 2020
Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization NIPS 2020
Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation AAAI 2020
Partial Multi-Label Learning via Credible Label Elicitation AAAI 2019
Multi-Label Learning with Regularization Enriched Label-Specific Features ACML 2019
CAFE: Adaptive VDI Workload Prediction with Multi-Grained Features AAAI 2019
Multi-Dimensional Classification via kNN Feature Augmentation AAAI 2019
Multi-View Multi-Label Learning with View-Specific Information Extraction IJCAI 2019
Towards Enabling Binary Decomposition for Partial Label Learning IJCAI 2018
Binary Linear Compression for Multi-label Classification IJCAI 2017
Solving the Partial Label Learning Problem: An Instance-Based Approach IJCAI 2015
Maximum Margin Partial Label Learning ACML 2015
Towards Class-Imbalance Aware Multi-Label Learning IJCAI 2015
Multi-Label Classification with Unlabeled Data: An Inductive Approach ACML 2013
Multi-Instance Multi-Label Learning with Application to Scene Classification NIPS 2006