主动学习
从未标注数据中选择更具信息量的样本，以降低标注成本并保持模型性能。
博士后研究员 · 南洋理工大学 · 计算与数据科学学院
唐英鹏
我的研究方向包括机器学习、数据挖掘、主动学习、标签高效学习、迁移学习、联邦学习，以及医学与药物发现中的人工智能。
我目前是南洋理工大学（Nanyang Technological University）计算与数据科学学院（College of Computing and Data Science）的博士后研究员，由 Prof. Alvin Chan 指导。我于 2017、2020 和 2024 年在 南京航空航天大学分别获得学士、硕士和博士学位，由 黄圣君教授指导。2017 至 2024 年，我是 PARNEC 组成员；2024 至 2025 年，我在 TrustFUL 实验室从事博士后研究，由 于涵教授指导。
研究概览
面向真实场景的数据高效机器学习
联邦学习
面向异构客户端和分布式学习目标，研究数据选择、特征选择与示例回放方法。
医学与药物发现 AI
将标签高效学习思想用于专家标注和实验数据昂贵的科学问题。
开源工具
建设可复用的主动学习研究工具，包括 ALiPy 主动学习工具包。
项目
研究软件与基准平台
学术成果
论文与专利
Federated Data and Feature Selection by Generalized CUR Decomposition.
Ying-Peng Tang, Zhuang Qi, Xiaoli Tang, Wei Zhuo, Sheng-Jun Huang, Han Yu.
In: Proceedings of the 43rd International Conference on Machine Learning, 2026.
Cross-View Lewis Weight Fusion Empowering Exemplar Replay for Federated Class-Incremental Learning.
Zhuang Qi, Ying-Peng Tang, Lei Meng, Xiaoxiao Li, Han Yu, Xiangxu Meng.
In: Proceedings of the 43rd International Conference on Machine Learning, 2026.
From Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneity.
Zhuang Qi, Ying-Peng Tang, Lei Meng, Guoqing Chao, Lei Wu, Han Yu, Xiangxu Meng.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2026.
Class-wise Balancing Data Replay for Federated Class-Incremental Learning.
Zhuang Qi, Ying-Peng Tang, Lei Meng, Han Yu, Xiaoxiao Li, Xiangxu Meng.
In: Proceedings of the 39th Annual Conference on Neural Information Processing Systems, 2025.
Active Learning for Multiple Target Models.
Sheng-Jun Huang, Yi Li and Ying-Peng Tang.
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.
Efficient Heterogeneity-Aware Federated Active Data Selection.
Ying-Peng Tang, Chao Ren, Xiaoli Tang, Sheng-Jun Huang, Lizhen Cui and Han Yu.
In: Proceedings of the 42nd International Conference on Machine Learning, 2025.
QFEVAL: Quantum Federated Ensembled Variational Adaptive Learning for Dynamic Security Assessment in Cyber-Physical Systems.
Chao Ren, Ying-Peng Tang, Yulan Gao, Xian Sun, Kun Fu, Mikael Skoglund, Zhao Yang Dong, Han Yu, Anran Li, Ming Xiao.
In: IEEE Journal on Selected Areas in Communications, 2025.
One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models.
Sheng-Jun Huang, Yi Li, Yiming Sun and Ying-Peng Tang.
In: Proceedings of the 12th International Conference on Learning Representations, 2024.
MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detection.
Dong Liang, Jing-Wei Zhang, Ying-Peng Tang and Sheng-Jun Huang.
In: IEEE Transactions on Geoscience and Remote Sensing, 2023.
Active Learning for Multiple Target Models.
Ying-Peng Tang and Sheng-Jun Huang.
In: Proceedings of the 36th Conference on Neural Information Processing Systems, 2022.
QBox: Partial Transfer Learning with Active Querying for Object Detection.
Ying-Peng Tang, Xiu-Shen Wei, Bo-Rui Zhao and Sheng-Jun Huang.
In: IEEE Transactions on Neural Networks and Learning Systems, 2021.
Dual Active Learning for Both Model and Data Selection.
Ying-Peng Tang and Sheng-Jun Huang.
In: Proceedings of the 30th International Joint Conference on Artificial Intelligence, 2021.
Self-paced active learning: query the right thing at the right time.
Ying-Peng Tang, Sheng-Jun Huang.
In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 2019.
荣誉
获奖情况
- 博士阶段国家奖学金
- “日内瓦国际发明展”金奖
- 江苏省优秀学术学位硕士学位论文
- 中国国际“互联网+”大学生创新创业大赛银奖（队长）
- 硕士阶段国家奖学金
学术服务
审稿与社区服务
会议审稿
AAAI、ICML、NeurIPS、CVPR、ICLR、IJCAI、PAKDD。
期刊审稿
IEEE Transactions on Knowledge and Data Engineering、Frontiers of Computer Science、Journal of Computer Science and Technology、Multimedia Systems。
PC Member
FL@FM-TheWebConf：2025、2026。