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.
- Lin Xiao, Xiangliang Zhang, Chi Huang, Mingyang Song and Liping Jing. Does Head Label Help for Long-Tailed Multi-Label Text Classification. In the proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021) (acceptance rate of 21%, 1692/7911).
- 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%)
- Zhuo Yang, Yufei Han, and Xiangliang Zhang. Characterizing the Evasion Attackability of Multi-label Classifiers. The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021) (acceptance rate of 21%, 1692/7911)
- 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).
- Guoxian Yu, Yuying Xing, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang, “Multiview Multi-Instance Multilabel Active Learning”. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021. DOI: 10.1109/TNNLS.2021.3056436
- Guoxian Yu, Xia Chen, Carlotta Domeniconi, Jun Wang, Zhao Li, Zili Zhang, Xiangliang Zhang. CMAL: Cost-effective Multi-label Active Learning by Querying Subexamples. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021. Early Access: 10.1109/TKDE.2020.3003899
- 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)
- Yufei Han, Yun Shen, Guolei Sun and Xiangliang Zhang: Multi-label Learning with Highly Incomplete Data via Collaborative Embedding. In Proceedings of the 24th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2018), pp. 1494-1503, London, UK, August 19 – 23, 2018 (long presentation, acceptance rate= 107/ 983 =10.9%)(overall acceptance rate=18.4%)[PDF][Bib][Slides].