The long-tail distribution, where a few head labels dominate while rare tail labels abound, poses a persistent challenge for large-scale Multi-Label Classification (MLC) in real-world data mining applications. Existing resampling and reweighting strategies often disrupt inter-label dependencies or require brittle hyperparameter tuning, especially as the label space expands to tens of thousands of labels. To address this issue, we propose Curiosity-Driven Game-Theoretic Multi-Label Learning (CD-GTMLL), a scalable cooperative framework that recasts long-tail MLC as a multi-player game - each sub-predictor ("player") specializes in a partition of the label space, collaborating to maximize global accuracy while pursuing intrinsic curiosity rewards based on tail label rarity and inter-player disagreement. This mechanism adaptively injects learning signals into under-represented tail labels without manual balancing or tuning. We further provide a theoretical analysis showing that our CD-GTMLL converges to a tail-aware equilibrium and formally links the optimization dynamics to improvements in the Rare-F1 metric. Extensive experiments across 7 benchmarks, including extreme multi-label classification datasets with 30,000+ labels, demonstrate that CD-GTMLL consistently surpasses state-of-the-art methods, with gains up to +1.6% P@3 on Wiki10-31K. Ablation studies further confirm the contributions of both game-theoretic cooperation and curiosity-driven exploration to robust tail performance. By integrating game theory with curiosity mechanisms, CD-GTMLL not only enhances model efficiency in resource-constrained environments but also paves the way for more adaptive learning in imbalanced data scenarios across industries like e-commerce and healthcare.
Curiosity Meets Cooperation: A Game-Theoretic Approach to Long-Tail Multi-Label Learning
School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区计算机科学与技术学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Department of Statistics and Data Science, National University of Singapore（新加坡国立大学统计与数据科学系）；NExT++ Research Centre, National University of Singapore（新加坡国立大学NExT++研究中心）
Advancing Enterprise Spatio-Temporal Forecasting Applications: Data Mining Meets Instruction Tuning of Language Models For Multi-modal Time Series Analysis in Low-Resource Settings
Neighborhood-Adaptive Generalized Linear Graph Embedding with Latent Pattern Mining
School of Computer and Information, Anhui Normal University（安徽师范大学计算机与信息学院）；Anhui Provincial Key Laboratory of Industrial Intelligent Data Security（安徽省工业智能数据安全重点实验室）；Institute of Automation, State Key Laboratory of Multimodal Artificial Intelligence Systems, Chinese Academy of Sciences（自动化研究所，多模态人工智能系统国家重点实验室，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Long-Tailed Out-of-Distribution Detection with Refined Separate Class Learning
Nanjing Agricultural University（南京农业大学）；Nanjing University（南京大学）；Shandong University（山东大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Southeast University（东南大学）
Long-Tailed Recognition Using Class-Balanced Experts
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Saarland Informatics Campus（萨尔兰信息学园区）；University of Maryland, College Park（马里兰大学帕克分校）；CISPA Helmholtz Center for Information Security（亥姆霍兹信息安全研究中心）
Recommender systems and reinforcement learning for human-building interaction and context-aware support: A text mining-driven review of scientific literature
National University of Singapore（新加坡国立大学）；Future Cities Lab Global（未来城市实验室全球中心）；Singapore-ETH Centre（新加坡-ETH中心）
Secure and Privacy-Preserving Federated Learning for Next-Generation Underground Mine Safety
2 School of Engineering；Applied Science, Washington State University, Richland, WA 99354, USA；3 Computer Science Department, Missouri University of Science；4 Explosive \& Mining Engineering Department, Missouri University of Science
SAIL: Scene-aware Adaptive Iterative Learning for Long-Tail Trajectory Prediction in Autonomous Vehicles
State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau（澳门大学智慧城市物联网国家重点实验室及土木与环境工程系）；State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science, University of Macau（澳门大学智慧城市物联网国家重点实验室及计算机与信息科学系）；Department of Automotive Engineering, Tsinghua University（清华大学汽车工程系）；State Key Laboratory of Internet of Things for Smart City and Departments of Civil and Environmental Engineering and Computer and Information Science, University of Macau（澳门大学智慧城市物联网国家重点实验室及土木与环境工程系和计算机与信息科学系）；Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology（香港科技大学土木与环境工程系）
