Lin-Han Jia @ LAMDA, NJU-CS
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贾林瀚 Lin-Han Jia
Ph.D. Student Supervisor: Professor Yu-Feng Li Email: jialh@lamda.nju.edu.cn Laboratory: Room 912, Computer Science Building, Xianlin Campus of Nanjing University [GitHub][Google Scholar][ZhiHu] |
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Biography
2022-present, Ph.D. student in the School of Computer Science at Nanjing University, advised by Prof. Yu-Feng Li, joining the Machine Learning and Data Mining (LAMDA) Group founded by Academician Zhi-Hua Zhou.
2018-2022, B.Sc. in the College of Computer Science and Technology at Jilin University, joining the Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education founded by Academician Xianghao Wang.
Research Interests
My main research focuses on self-supervised, semi-supervised, weakly supervised and unsupervised learning. I also conduct in-depth research on the knowledge-and-data dual-driven learning paradigm, with a core focus on fundamental research into model training that fully leverages unlabeled data and prior knowledge.
My specific research interests are listed as follows:
- Inherent correlation between pre-training and downstream tasks: To define the capability boundaries of various unsupervised pre-training approaches, and explore the limitations of existing foundation models in downstream tasks as well as the correlation between pre-training data and target tasks.
- Selection and synthesis of pre-training data: To build a training data selection and synthesis system based on downstream objectives and prior knowledge, establish a dynamic feedback mechanism linking tasks to data, and realize large-scale training data management via semantic vector models and vector database optimization.
- Quantifiable training decision framework: To construct a quantitative framework for model training decision-making, enabling quantitative assessment of the benefits of training decisions before costly training runs, so as to reduce the time and computing overhead of large-scale training.
- Workflow reasoning for pre-training data and tasks: To shift automated training frameworks from the search paradigm to the reasoning paradigm, and achieve adaptive design of pre-training data pipelines and task workflows.
Publications
Conference Papers
Lin-Han Jia, Si-Yu Han, Wen-Chao Hu, Jie-Jing Shao, Wen-Da Wei, Zhi Zhou, Lan-Zhe Guo, Yu-Feng Li. Quantitative Estimation of Target Task Performance from Unsupervised Pretext Task in Semi/Self-Supervised Learning. In: Proceedings of the 43rd International Conference on Machine Learning (ICML'26). 2026. CCF-A. [Paper][Code]
Hong-Jie You, Jie-Jing Shao, Xiao-Wen Yang, Lin-Han Jia, Lan-Zhe Guo, Yu-Feng Li. Pianist Transformer: Towards Expressive Piano Performance Rendering via Scalable Self-Supervised Pre-Training. In: Proceedings of the 43rd International Conference on Machine Learning (ICML'26). 2026. CCF-A. [Paper]
Hong-Jie You, Jie-Jing Shao, Xiao-Wen Yang, Zhi-Fan Wu, Lin-Han Jia, Lan-Zhe Guo, Yu-Feng Li. FormalImG: Evaluating Structural Compositional Generalization for T2I Models. In: Proceedings of the 43rd International Conference on Machine Learning (ICML'26) Workshop on Combining Theory and Benchmarks: Towards A Virtuous Cycle to Understand and Guarantee Foundation Model Performance. 2026. [Paper]
Wen-Chao Hu, Qi-Jie Li, Lin-Han Jia, Cunjing Ge, Yu-Feng Li, Yuan Jiang, Zhi-Hua Zhou. Curriculum Abductive Learning. In: the 39th Annual Conference on Neural Information Processing Systems (NeurIPS'25). 2025. CCF-A. [Paper]
Shi-Yu Tian, Zhi Zhou, Kun-Yang Yu, Ming Yang, Lin-Han Jia, Lan-Zhe Guo, Yu-Feng Li. VC Search: Bridging the Gap Between Well-Defined and Ill-Defined Problems in Mathematical Reasoning. In: Proceedings of the 30th Conference on Empirical Methods in Natural Language (EMNLP'25). 2025. CCF-B. Oral Presentation. [Paper]
Si-Yu Han, Lin-Han Jia, Yu-Feng Li. 基于正则的稳健数据重载量子学习模型. In: Proceedings of the 20th Chinese Conference on Machine Learning (CCML'25). 2025. Best Paper Award Nomination.
Lin-Han Jia, Wen-Chao Hu, Jie-Jing Shao, Lan-Zhe Guo, Yu-Feng Li. Verification Learning: Make Unsupervised Neuro-Symbolic System Feasible. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25). 2025. CCF-A. [Paper][Code]
Xiao-Wen Yang, Jie-Jing Shao, Lan-Zhe Guo, Bo-Wen Zhang, Zhi Zhou, Lin-Han Jia, Wang-Zhou Dai, Yu-Feng Li. Neuro-Symbolic Artificial Intelligence: Towards Improving the Reasoning Abilities of Large Language Models. In: Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI'2025 Survey Track). 2025. CCF-B. [Paper]
Lin-Han Jia, Lan-Zhe Guo, Zhi Zhou, and Yu-Feng Li. Realistic Evaluation of Semi-Supervised Learning Algorithms in Open Environments. In: Proceedings of the 12th International Conference on Learning Representations (ICLR'24). 2024. CCF-A. Spotlight Presentation. [Paper][Code]
Zhi-Zhou, Lan-Zhe Guo, Lin-Han Jia, Ding-Chu Zhang, Yu-Feng Li. ODS: Test-Time Adaptation in the Presence of Open-World Data Shift. In: Proceedings of the 40th International Conference on Machine Learning (ICML'23). 2023. CCF-A. Oral Presentation. [Paper]
Lin-Han Jia, Lan-Zhe Guo, Zhi Zhou, Jie-Jing Shao, Yuke Xiang, Yu-Feng Li. Bidirectional Adaptation for Robust Semi-Supervised Learning with Inconsistent Data Distributions. In: Proceedings of the 40th International Conference on Machine Learning (ICML'23). 2023. CCF-A. Oral Presentation. [Paper][Code]
Journal Papers
Lan-Zhe Guo, Lin-Han Jia, Jie-Jing Shao, Yu-Feng Li. Robust Semi-Supervised Learning in Open Environments. Frontiers of Computer Science. 2025. CCF-B. [Paper]
Lin-Han Jia, Lan-Zhe Guo, Zhi Zhou, and Yu-Feng Li. LAMDA-SSL: A Comprehensive Semi-Supervised Learning Toolkit. Science CHINA Information Science. 2023. CCF-A. [Paper][Code][Slide]
Preprint
Lin-Han Jia, Jie-Jing Shao, Wen-Chao Hu, Zhi Zhou, Lan-Zhe Guo, Yu-Feng Li. Learning Task Design Based on Fuzzy Logical Reasoning. 2026. [Paper]
Wen-Chao Hu, Lin-Han Jia, Cunjing Ge, Yu-Feng Li, Yuan Jiang, and Zhi-Hua Zhou. ABL-Forest: Localized Abductive Learning with Formal Guarantees. 2026.
Rui-Zhi Qin, Jie-Jing Shao, Xiao-Wen Yang, Lin-Han Jia, Lan-Zhe Guo, Yu-Feng Li. COAST: Self-Correction Learning for Mastering Constraint Satisfaction in Travel Planning. 2026.
Lin-Han Jia, Si-Yu Han, Lan-Zhe Guo, Zhi Zhou, Zhao-Long Li, Yu-Feng Li, Zhi-Hua Zhou. A Smooth Transition Between Induction and Deduction: Fast Abductive Learning Based on Probabilistic Symbol Perception. arXiv preprint arXiv:2502.12919. 2025. [Paper]
Lin-Han Jia, Lan-Zhe Guo, Zhi Zhou, Si-Yu Han, Zi-Wen Li, Yu-Feng Li. Detecting Scarce and Sparse Anomalous: Solving Dual Imbalance in Multi-Instance Learning. arXiv preprint arXiv:2503.13562. 2025. [Paper]
Si-Yu Han, Lin-Han Jia, Lan-Zhe Guo. Multiple Embeddings for Quantum Machine Learning. arXiv preprint arXiv:2503.22758. 2025. [Paper]
Software
- LAMDA-SSL, lead contributor, a semi-supervised learning toolkit, with over 29,000 downloads via pip. [Project] [Documentation] [Examples]
- ABLkit, contributor, an abductive learning toolkit, with over 22,000 downloads via pip. [Project] [Documentation] [Examples]
Awards & Honors
CCF Doctoral Student Fellowship (10 recipients nationwide), 2026
Best Paper Nomination, CCML, 2025
LAMDA Outstanding Talent Award (3 recipients), 2025
Elite Doctoral Scholarship, 2024
National Scholarship, 2023
Outstanding Graduate, 2023–2025
Outstanding Undergraduate Graduate, 2022
Excellent Undergraduate Thesis Award, 2022
CCF Outstanding Student (94 recipients nationwide), 2021
Silver Medal, ACM-ICPC Asia Regional Contest, 2021
Gold Medal, CCPC Provincial Collegiate Programming Contest, 2020
Finalist Prize(Nomination for Outstanding Winner, Top 1.5% worldwide), Mathematical Contest in Modeling, 2020
First Prize, China Undergraduate Mathematical Contest in Modeling (Provincial Round), 2019
Academic Services
- Senior Program Committee Member: IJCAI 2025
- Program Committee Member: NeurIPS 2023–2025, AAAI 2024–2026, ICLR 2024–2026, IJCAI 2024–2026, ICML 2025–2026, KDD 2024–2026, CVPR 2026, ECCV 2026
- Conference Volunteer: ICLR 2024, MLA 2022–2023
- Journal Reviewer: Machine Learning, Neurocomputing, Neural Networks
- Teaching Assistant: Advanced Machine Learning (Graduate Course, Nanjing University), 2024–2025