Multi-label, Multi-view Learning

This is a collection of our recent publications that address the challenging multi-label classification problem, the attackability of multi-label learning, and the information fusion in multi-view learning.

  • Xiaochuan Gou, Xiangliang Zhang. Telecommunication Traffic Forecasting via Multi-task Learning. ACM International Conference on Web Search and Data Mining (WSDM 2023). Singapore February 27 to March 3, 2023.
  • Zhuo Yang, Yufei Han and Xiangliang Zhang. Attack Transferability Characterization for Adversarially Robust Multi-label Classification. Accepted by ECML/PKDD 2021. Virtual Conference. Aug 13-17, 2021. (Acceptance Rate = 147/685 = 21%)
  • Yuanlin Yang, Guoxian Yu, Carlotta Domeniconi, Xiangliang Zhang. Deep Multi-type Objects Muli-view Multi-instance Multi-label Learning.  SIAM Conference on Data Mining (SDM), Virtual Conference, April 29 – May 1, 2021 (acceptance rate of 21%, 85/400).
  • Yingting Yu, Guoxian Yu, Jun Wang, Carlotta Domeniconi, and Xiangliang Zhang, “Partial Multi-Label Learning using Label Compression”. Accepted by the 20th IEEE International Conference on Data Mining (ICDM 2020), November 17-20, 2020, Sorrento, Italy. (Regular paper) (Acceptance rate = 91 (regular)  / 930 submissions = 9.8%).​
  • Shaowei Wei, Guoxian Yu, Jun Wang, Carlotta Domeniconi, and Xiangliang Zhang, “Deep Incomplete Multi-View Multiple Clusterings”. Accepted by the 20th IEEE International Conference on Data Mining (ICDM 2020), November 17-20, 2020, Sorrento, Italy. (Regular paper) (Acceptance rate = 91 (regular)  / 930 submissions = 9.8%).
  • Yuanlin Yang, Guoxian Yu, Jun Wang, Carlotta Domeniconi, and Xiangliang Zhang. Multi-type Objects Multi-view Multi-instance Multi-label Learning. Accepted by the 20th IEEE International Conference on Data Mining (ICDM 2020), November 17-20, 2020, Sorrento, Italy. (short paper) (Acceptance rate = (91 (regular) + 92 (short)) / 930 submissions = 19.7%).​
  • Uchenna Akujuobi, Han Yufei, Qiannan Zhang, Xiangliang Zhang. Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification. In the Proceedings of  19th IEEE International Conference on Data Mining (ICDM 2019), November 8-11, 2019, Beijing, China (Regular paper, Acceptance rate= 95/1046 = 9.08%).​ ​ (paper at arXiv​)