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Deng-Bao Wang 王登豹 I am an assistant professor at the School of Computer Science and Engineering, Southeast University. I earned my PhD from Southeast University in 2024, advised by Prof. Min-Ling Zhang. I am a member of PALM group. My research interests mainly include artificial intelligence, machine learning and data mining. I'm currently working on weakly supervised learning and uncertainty calibration of deep models. Additionally, I'm also interested in gaining a deeper understanding of modern neural networks through insightful experiments. Email / Address / Google Scholar / Our Reading Group / 中文主页 |
Teaching |
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Introduction to Artificial Intelligence, Autumn 2025 [course page] Discrete Mathematics, Spring 2026 [course page] |
Publications |
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Uncertainty Calibration in Deep Learning: Methods, Emerging Challenges, and LLM Frontiers
M.-L. Zhang, Deng-Bao Wang Journal of Computer Science and Technology, 2026, 41: 318-340 PDF |
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MotionDuet: Dual-Conditioned 3D Human Motion Generation with Video-Regularized Text Learning
Y.-Y. Zhang, T. Sun, F. Cheng, Deng-Bao Wang*, X. Cai, M.-L. Zhang, H. Kim IEEE/CVF Conference on Computer Vision and Pattern Recognition Findings (CVPR Findings), 2026 |
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Semi-supervised Partial Label Learning via Label Confidence Recovery
X.-R. Yu, Deng-Bao Wang, M.-L. Zhang Pattern Recognition (PR), 2026, 178: Article 113387 |
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Investigating the confidence calibratability of deep neural networks (In Chinese)
Deng-Bao Wang, M.-L. Zhang SCIENTIA SINICA Informationis, 2025, 55: 2289–2303 |
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Wrapped Partial Label Dimensionality Reduction via Dependence Maximization
X.-R. Yu, Deng-Bao Wang, M.-L. Zhang International Joint Conference on Artificial Intelligence (IJCAI), 2025, 6913-6921 |
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Simplified Graph Contrastive Learning Model Without Augmentation
Y. Lin, G. Lyu, H. Cai, Deng-Bao Wang, H. Wang, Z. Yang IEEE Transactions on Knowledge and Data Engineering, 2025, 37(10): 6159-6172 |
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Calibration Bottleneck: Over-compressed Representations are Less Calibratable
Deng-Bao Wang, M.-L. Zhang International Conference on Machine Learning (ICML), 2024 PDF Code |
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Distilling Reliable Knowledge for Instance-Dependent Partial Label Learning
D.-D. Wu†, Deng-Bao Wang† (equal contribution), M.-L. Zhang AAAI Conference on Artificial Intelligence (AAAI), 2024 PDF Code Appendix |
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Student Loss: Towards the Probability Assumption in Inaccurate Supervision
S. Zhang, J.-Q. Li, H. Fujita, Y.-W. Li, Deng-Bao Wang, T.-T. Zhu, M.-L. Zhang, C.-Y. Liu IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024, 46(6): 4460-4475 |
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Dimensionality Reduction for Partial Label Learning: A Unified and Adaptive Approach
X.-R. Yu, Deng-Bao Wang, M.-L. Zhang IEEE Transactions on Knowledge and Data Engineering (TKDE), 2024, 36(8): 3765-3782 |
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Learning from Noisy Labels via Dynamic Loss Thresholding
H. Yang, Y.-Z. Jin, Z.-Y. Li, Deng-Bao Wang, X. Geng, M.-L. Zhang IEEE Transactions on Knowledge and Data Engineering (TKDE), 2024, 36(11): 6503-6516 |
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Multiple-Instance Learning from Triplet Comparison Bags
S. Shu, Deng-Bao Wang, S. Yuan, H. Wei, J. Jiang, L. Feng, M.-L. Zhang ACM Transactions on Knowledge Discovery from Data (TKDD), 2024, 18(4): Article 90 |
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Partial Label Learning with Emerging New Labels
X.-R. Yu, Deng-Bao Wang, M.-L. Zhang Machine Learning (MLJ), 2024, 113(4): 1549-1565 |
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On the Pitfall of Mixup for Uncertainty Calibration
Deng-Bao Wang, L. Li, P. Zhao, P.-A. Heng, M.-L. Zhang IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 PDF Code Appendix |
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Partial-Label Regression
X. Cheng, Deng-Bao Wang, L. Feng, M.-L. Zhang, B. An AAAI Conference on Artificial Intelligence (AAAI), 2023, 7140-7147 |
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Adaptive Graph Guided Disambiguation for Partial Label Learning
Deng-Bao Wang, M.-L. Zhang, Li Li IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022 PDF Code Appendix |
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Revisiting Consistency Regularization for Deep Partial Label Learning
D.-D. Wu†, Deng-Bao Wang†, M.-L. Zhang International Conference on Machine Learning (ICML), 2022 PDF Code |
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Rethinking Calibration of Deep Neural Networks: Don't Be Afraid of Overconfidence
Deng-Bao Wang, L. Feng, M.-L. Zhang Advances in Neural Information Processing Systems (NeurIPS), 2021 PDF Code Appendix |
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Learning from Complementary Labels via Partial-Output Consistency Regularization
Deng-Bao Wang, L. Feng, M.-L. Zhang International Joint Conference on Artificial Intelligence (IJCAI), 2021 PDF Code |
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Learning from Noisy Labels with Complementary Loss Functions
Deng-Bao Wang, Y. Wen, L. Pan, M.-L. Zhang AAAI Conference on Artificial Intelligence (AAAI), 2021 PDF Code Appendix |
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Adaptive Graph Guided Disambiguation for Partial Label Learning
Deng-Bao Wang, Li Li, M.-L. Zhang ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019 PDF Code |
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Multi-View Multi-Label Learning with View-Specific Information Extraction
X. Wu, Q.-G. Chen, Y. Hu, Deng-Bao Wang, X. Chang, X. Wang, M.-L. Zhang International Joint Conference on Artificial Intelligence (IJCAI), 2019, 3884-3890 |
Honors |
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中国计算机学会博士学位论文激励计划 (2025) CCF人工智能与模式识别专委会博士学位论文激励计划 (2025) 首届“NSFC博士生基金”项目 (2023) DAAD AInet Fellow (2023) National Scholarship (2022, 2018) Tencent Rhino-Bird Elite Training Program (2022) Special Freshman Scholarship for PhD Students (2019) |
Services |
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Conference program committee member for ICLR (2024, 2025, 2026) ICML (2022, 2023, 2024, 2025, 2026), NeurIPS (2023, 2024, 2025), AAAI (2021, 2022, 2024, 2025, 2026), IJCAI (2022, 2023, 2024, 2025, 2026), KDD (2024, 2025, 2026), etc. Journal reviewer for IEEE TPAMI, IEEE TNNLS, SCIENCE CHINA Information Sciences, ACM TIST, ACM TKDD, IEEE TMM, etc. |