| 2025 | IJCAI | Wrapped Partial Label Dimensionality Reduction via Dependence Maximization. | Xiang-Ru Yu, Deng-Bao Wang, Min-Ling Zhang |
| 2024 | AAAI | Distilling Reliable Knowledge for Instance-Dependent Partial Label Learning. | Dong-Dong Wu, Deng-Bao Wang, Min-Ling Zhang |
| 2024 | ICML | Calibration Bottleneck: Over-compressed Representations are Less Calibratable. | Deng-Bao Wang, Min-Ling Zhang |
| 2023 | AAAI | Partial-Label Regression. | Xin Cheng, Deng-Bao Wang, Lei Feng, Min-Ling Zhang, Bo An |
| 2023 | CVPR | On the Pitfall of Mixup for Uncertainty Calibration. | Deng-Bao Wang, Lanqing Li, Peilin Zhao, Pheng-Ann Heng, Min-Ling Zhang |
| 2022 | ICML | Revisiting Consistency Regularization for Deep Partial Label Learning. | Dong-Dong Wu, Deng-Bao Wang, Min-Ling Zhang |
| 2022 | ICONIP | Partial Label Learning with Gradually Induced Error-Correction Output Codes. | Yu-Xuan Shi, Deng-Bao Wang, Min-Ling Zhang |
| 2021 | AAAI | Learning from Noisy Labels with Complementary Loss Functions. | Deng-Bao Wang, Yong Wen, Lujia Pan, Min-Ling Zhang |
| 2021 | IJCAI | Learning from Complementary Labels via Partial-Output Consistency Regularization. | Deng-Bao Wang, Lei Feng, Min-Ling Zhang |
| 2019 | KDD | Adaptive Graph Guided Disambiguation for Partial Label Learning. | Deng-Bao Wang, Li Li, Min-Ling Zhang |