Research

Multi-Task Learning
Multi-Task Learning (MTL) is an important and promising direction of Machine Learning, due to its flexibility in data representation and ubiquity in modern real-world applications. In MTL, multiple targets/tasks are correlated with each other. A number of traditional Machine Learning problems and recently emerging real-world applications can be considered as MTL problems. For example, in web page classification, each web page possibly relates with multiple functional classes (tasks), like space explorer, JAXA and Ryugu, etc. To utilize the information among tasks and samples, MTL learns related tasks together by correctly modeling task relationship.

Publications:

• Luhuan Fei, Lu Sun, Mineichi Kudo and Keigo Kimura, “Structured Sparse Multi-Task Learning with Generalized Group Lasso”, in Proceedings of the 26th European Conference on Artificial Intelligence (ECAI 2023), 2023, Krakow, Poland.
  
• Xinyi Wang, Lu Sun, Canh Hao Nguyen and Hiroshi Mamitsuka, “Multiplicative Sparse Tensor Factorization for Multi-View Multi-Task Learning”, in Proceedings of the 26th European Conference on Artificial Intelligence (ECAI 2023), 2023, Krakow, Poland.
  
• Jiachun Jin, Jiankun Wang, Lu Sun, Jie Zheng and Mineichi Kudo, “Grouped Multi-Task Learning with Hidden Tasks Enhancement”, in Proceedings of the 26th European Conference on Artificial Intelligence (ECAI 2023), 2023, Krakow, Poland.
  
• Jiankun Wang and Lu Sun, “Multi-Task Personalized Learning with Sparse Network Lasso”, in Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI 2022), 3516-3522, 2022, Vienna, Austria.
  
• Lu Sun, Canh Hao Nguyen, Hiroshi Mamitsuka, “Fast and Robust Multi-View Multi-Task Learning via Group Sparsity”, in Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), 3499-3505, 2019, Macao, China.
  
• Lu Sun, Canh Hao Nguyen, Hiroshi Mamitsuka, “Multiplicative Sparse Feature Decomposition for Efficient Multi-View Multi-Task Learning”, in Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), 3506-3512, 2019, Macao, China.
  
  
  

Multi-Label Classificaiton
Unlike traditional single-label classification where each instance is associated with only one label, Multi-Label Classification (MLC) refers to the problems assigning multiple labels to a single test instance. MLC can be seen in a wide range of real-world applications such as text categorization, semantic image classification, bioinformatics analysis and video annotation. In fact, MLC is ubiquitous in real-world problems. For example, a news article is possibly relevant to multiple topics, like “science”, “technology”, “economics”, “politics”, etc; a single image is probably associated with a set of semantic concepts, like “sky”, “sea”, “field”, “building”, etc.

Publications:

• Weijia Lin, Jiankun Wang, Lu Sun, Mineichi Kudo and Keigo Kimura, “Multi-Label Personalized Classification via Exclusive Sparse Tensor Factorization “, in Proceedings of the 23rd IEEE International Conference on Data Mining (ICDM 2023), 2023, Shanghai, China.
  
• Haruhi Mizuguchi, Keigo Kimura, Mineichi Kudo and Lu Sun, “Partial Multi-label Learning with a Few Accurately Labeled Data”, in Proceedings of the 20th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2023), 2023, Jakarta, Indonesia.
  
• Zhiwei Li, Zijian Yang, Lu Sun, Mineichi Kudo and Keigo Kimura, “Incomplete Multi-View Weak-Label Learning with Noisy Features and Imbalanced Labels”, in Proceedings of the 20th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2023), 2023, Jakarta, Indonesia.
  
• Lu Sun and Mineichi Kudo, “Multi-Label Classification by Polytree-Augmented Classifier Chains with Label-Dependent Features”, Pattern Analysis and Applications, 22(3), 1029-1049, 2019. DOI:10.1007/s10044-018-0711-6
  
• Lu Sun and Mineichi Kudo, “Optimization of Classifier Chains via Conditional Likelihood Maximization”, Pattern Recognition, 74: 503-517, 2018. DOI:10.1016/j.patcog.2017.09.034
  
• Lu Sun, Mineichi Kudo and Keigo Kimura, “READER: Robust Semi-Supervised Multi-Label Dimension Reduction”, IEICE Transactions on Information and Systems, E100-D(10): 2597-2604, 2017. DOI:10.1587/transinf.2017EDP7184
  
• Lu Sun, Mineichi Kudo and Keigo Kimura, “Multi-Label Classification with Meta-Label-Specific Features”, in Proceedings of the 23rd International Conference on Pattern Recognition (ICPR 2016), 1612-1617, 2016, Cancun, Mexico.
  
• Lu Sun, Mineichi Kudo and Keigo Kimura, “A Scalable Clustering-Based Local Multi-Label Classification Method”, in Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI 2016), 261-268, 2016, The Hague, Netherlands.
  
• Lu Sun and Mineichi Kudo, “Polytree-Augmented Classifier Chains for Multi-Label Classification”, in Proceedings of the 24th International Joint Conference of Artificial Intelligence (IJCAI 2015), 3834-3840, 2015, Buenos Aires, Argentina.
  
• Keigo Kimura, Lu Sun and Mineichi Kudo, “MLC Toolbox: A MATLAB/OCTAVE Library for Multi-Label Classification”, CoRR abs/1704.02592, 2017. (preprinted in arXiv)