Active and label-efficient learning
Selecting the right examples to annotate, train on, or transfer from so that models can learn reliably when labeled data is scarce.
Machine learning researcher
Ying-Peng Tang
Research Fellow · College of Computing and Data Science · NTU Singapore
I study machine learning methods that make better use of limited, distributed, and expensive-to-label data. My research spans active learning, label-efficient learning, transfer learning, federated learning, and AI for medicine and drug discovery.
I am currently supervised by Prof. Alvin Chan. I received my B.Sc., Master and Ph.D. degrees from Nanjing University of Aeronautics and Astronautics, advised by Prof. Sheng-Jun Huang. I was a member of the PARNEC Group from 2017 to 2024, and a research fellow in TrustFUL Lab, hosted by Prof. Han Yu, from 2024 to 2025.
Active learning
Label-efficient learning
Transfer learning
Federated learning
AI for medicine
Research focus
Data-efficient learning for settings where labels, privacy, and distribution shift matter.
Federated data selection
Designing selection and replay methods for heterogeneous clients, decentralized objectives, and changing local data distributions.
Scientific and medical AI
Bringing data-efficient learning ideas to domains where expert annotation and experimental feedback are costly.
Research software
Tools for reproducible active learning experiments.
Open-source toolbox
ALiPy: Active Learning in Python
ALiPy provides a reusable implementation base for active learning experiments, including multiple query strategies and experimental settings for comparison studies.
Publications
Selected and recent research outputs.
Recent papers and representative earlier work. For the full record, see Google Scholar.
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.
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.
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.
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.
Academic profile
Recognition and service.
Recognition
- National Scholarship for Ph.D. Candidate
- International Exhibition of Inventions Geneva Gold Award
- Excellent Master's Thesis Award, Jiangsu Province
- China International Internet+ Innovation Competition Silver Award, team leader
- National Scholarship for Master's Student
Academic service
Conference reviewer
AAAI, ICML, NeurIPS, CVPR, ICLR, IJCAI, PAKDD.
Journal reviewer
IEEE TKDE, Frontiers of Computer Science, Journal of Computer Science and Technology, Multimedia Systems.
Program committee
FL@FM-TheWebConf: 2025, 2026.