A Concise Yet Effective Model for Non-Aligned Incomplete Multi-View and Missing Multi-Label Learning. - Xiang Li 119; Songcan Chen (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

No Regularization is Needed: An Efficient and Effective Model for Incomplete Label Distribution Learning. - Xiang Li 119; Songcan Chen (CoRR, 2023)

No Regularization Is Needed: Efficient and Effective Incomplete Label Distribution Learning. - Xiang Li 119; Songcan Chen (IJCAI, 2024)

A Survey on Incomplete Multi-label Learning: Recent Advances and Future Trends. - Xiang Li 119; Jiexi Liu 001; Xinrui Wang 003; Songcan Chen (CoRR, 2024)

Pushing one pair of labels apart each time in multi-label learning: from single positive to full labels. - Xiang Li 119; Xinrui Wang 003; Songcan Chen (Sci. China Inf. Sci., 2025)