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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] |
Selected Publications († denotes equal contribution) |
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Calibration Bottleneck: Over-compressed Representations are Less Calibratable
Deng-Bao Wang, Min-Ling Zhang International Conference on Machine Learning (ICML), 2024 PDF Code We empirically observed a U-shaped pattern on calibratability of intermediate features, spanning from the lower to the upper layers. |
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On the Pitfall of Mixup for Uncertainty Calibration
Deng-Bao Wang, Lanqing Li, Peilin Zhao, Pheng-Ann Heng, Min-Ling Zhang IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 PDF Code Appendix We pointed out the pitfall of Mixup on calibration and propose a simple yet effective strategy named Mixup Inference in Training. |
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Adaptive Graph Guided Disambiguation for Partial Label Learning
Deng-Bao Wang, Min-Ling Zhang, Li Li IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022 PDF Code Appendix |
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Rethinking Calibration of Deep Neural Networks: Don't Be Afraid of Overconfidence
Deng-Bao Wang, Lei Feng, Min-Ling Zhang Advances in Neural Information Processing Systems (NeurIPS), 2021 PDF Code Appendix We for the first time found that despite those regularized models are better calibrated, they suffer from not being calibratable. |
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Learning from Complementary Labels via Partial-Output Consistency Regularization
Deng-Bao Wang, Lei Feng, Min-Ling Zhang International Joint Conference on Artificial Intelligence (IJCAI), 2021 PDF Code |
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), NeurIPS (2023, 2024, 2025), AAAI (2021, 2022, 2024, 2025), IJCAI (2022, 2023, 2024, 2025), KDD (2024, 2025), etc. Journal reviewer for IEEE TPAMI, IEEE TNNLS, SCIENCE CHINA Information Sciences, ACM TIST, ACM TKDD, IEEE TMM, etc. |