Selected Publications

(* Equal contribution)

1. Jiayu Hu, Senlin Shu, Beibei Li, Tao Xiang, Zhongshi He.
   An Unbiased Risk Estimator for Partial Label Learning with Augmented Classes.
   ACM Transactions on Intelligent Systems and Technology (TIST), 2025.
   

2. Zixi Wei*, Senlin Shu*, Yuzhou Cao, Hongxin Wei, Bo An, Lei Feng.
   Consistent Multi-Class Classification from Multiple Unlabeled Datasets.
   Proceedings of the 12th International Conference on Learning Representations (ICLR'24), 2024.
   (Acceptance Rate: 5.01%, Spotlight)

3. Shengjie Zhou, Senlin Shu, Haobo Wang, Hongxin Wei, Tao Xiang, Beibei Li.
   Multiple-Instance Learning from Pairwise Comparison Bags.
   ACM Transactions on Intelligent Systems and Technology (TIST), 2024.
   

4. Senlin Shu, Deng-Bao Wang, Suqing Yuan, Hongxin Wei, Jiuchuan Jiang, Lei Feng, Min-Ling Zhang.
   Multiple-Instance Learning from Triplet Comparison Bags.
   ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 18, no. 4, pp. 1-18, 2024.
   

5. Senlin Shu, Haobo Wang, Zhuowei Wang, Bo Han, Tao Xiang, Bo An, Lei Feng.
   Online Binary Classification from Similar and Dissimilar Data.
   Machine Learning (MLJ), vol. 113, no. 6, pp. 3463-3484, 2024.
   

6. Beibei Li, Senlin Shu, Beihong Jin, Tao Xiang, Yiyuan Zheng.
   Gemini: A Dual-Task Co-training Model for Partial Label Learning.
   Proceedings of the 36th Australasian Joint Conference on Artificial Intelligence (AJCAI'23), pp.328-340, 2023.
   

7. Senlin Shu, Shuo He, Haobo Wang, Hongxin Wei, Tao Xiang, Lei Feng.
   A Generalized Unbiased Risk Estimator for Learning with Augmented Classes.
   Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), pp. 9829-9836, 2023.
   

8. Lei Feng, Senlin Shu, Yuzhou Cao, Lue Tao, Hongxin Wei, Tao Xiang, Bo An, Gang Niu.
   Multiple-Instance Learning from Unlabeled Bags with Pairwise Similarity.
   IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 35, no. 11, pp. 11599-11609, 2023. （导师一作，本人二作）
   

9. Senlin Shu, Fengmao Lv, Yan Yan, Li Li, Shuo He, Jun He.
   Incorporating Multiple Cluster Centers for Multi-Label Learning.
   Information Science (INS), 590: 60-73, 2022.
   

10. Lei Feng, Jun Huang, Senlin Shu, Bo An.
    Regularized Matrix Factorization for Multi-label Learning with Missing Labels.
    IEEE Transactions on Cybernetics (TCYB), vol. 52, no. 5, pp. 3710-3721, 2022.
    

11. Lei Feng, Senlin Shu, Yuzhou Cao, Lue Tao, Hongxin Wei, Tao Xiang, Bo An, Gang Niu.
    Multiple-Instance Learning from Similar and Dissimilar Bags.
    Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'21), pp.374-382, 2021.
    

12. Lei Feng, Senlin Shu, Nan Lu, Miao Xu, Bo Han, Gang Niu, Bo An, Masashi Sugiyama.
    Pointwise Binary Classification with Pairwise Confidence Comparisons.
    Proceedings of the Thirty-Eighth International Conference on Machine Learning (ICML'21), pp.3252-3262, 2021.
    

13. Senlin Shu, Zhuoyi Lin, Yan Yan, Li Li.
    Learning from Multi-Class Positive and Unlabeled Data.
    Proceedings of the IEEE International Conference on Data Mining (ICDM'20), pp.1256-1261, 2020.
    

14. Lei Feng, Senlin Shu, Zhuoyi Lin, Fengmao Lv, Bo An, Li Li.
    Can Cross Entropy Loss be Robust to Label Noise?
    Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI'20), pp.2206-2212, 2020.
    

15. Shuo He, Ke Deng, Li Li, Senlin Shu, Li Liu.
    Discriminatively Relabel for Partial Multi-Label Learning.
    Proceedings of the IEEE International Conference on Data Mining (ICDM'19), pp.280-288, 2019.