Jie-Jing Shao @ LAMDA, NJU-CS
Biography
I am a graduate student from Department of Computer Science & Technology in Nanjing University and a member of LAMDA Group, which is led by professor Zhi-Hua Zhou.
I received my B.Sc. degree from College of Computer Science and Technology (Tang Aoqing Honors Program in Science) at Jilin University in 2019. In the same year, I was admitted to study for a M.Sc. degree in Nanjing University without entrance examination. In 2022, I started to pursue a Ph.D. degree at Nanjing University.
Research Interests
My long-term goal is to build practical and robust machine learning systems in real world.
My current interest lies in topics related to Large Language Model Agent and Neuro-Symbolic Learning, especially on improving their generalization and data efficiency.
Selected Works
(* denotes equal contributions)
Jie-Jing Shao*, Xiao-Wen Yang*, Bo-Wen Zhang*, Bai-Zhi Chen, Wen-Da Wei, Guohao Cai, Zhenhua Dong, Lan-Zhe Guo, Yu-Feng Li. ChinaTravel: A Real-World Benchmark for Language Agent in Chinese Travel Planning. LLM Evals Workshop @ NeurIPS 2025. (Webpage, Code)
Jie-Jing Shao*, Hong-Jie You*, Guohao Cai, Quanyu Dai, Zhenhua Dong, Lan-Zhe Guo. Breaking the Self-Evaluation Barrier: Reinforced Neuro-Symbolic Planning with Large Language Models. In: Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI'25).
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'25 Survey Track).
Jie-Jing Shao*, Hao-Ran Hao*, Xiao-Wen Yang, Yu-Feng Li. Abductive Learning for Neuro-Symbolic Grounded Imitation. In: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'25). (Webpage, Code, Slides)
Presented at PAKDD 2024 Workshop on Robust Machine Learning in Open Environments, Best Paper Award (Slides)
Jie-Jing Shao*, Hao-Sen Shi*, Lan-Zhe Guo, Yu-Feng Li. Offline Imitation Learning with Model-based Reverse Augmentation. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'24). (Webpage, Code, Slides)
Jie-Jing Shao, Hao-Sen Shi, Tian Xu, Lan-Zhe Guo, Yang Yu, Yu-Feng Li. Offline Imitation Learning without Auxiliary High-quality Behavior Data. Preprint, 2023.
Jie-Jing Shao*, Jiang-Xin Shi*, Xiao-Wen Yang*, Lan-Zhe Guo, Yu-Feng Li. Investigating the Limitation of CLIP Models: The Worst-performing Categories. Preprint, 2023.
Jie-Jing Shao*, Xiao-Wen Yang*, Lan-Zhe Guo. Open-Set Learning under Covariate Shift. Machine Learning, 2024.
Jie-Jing Shao, Lan-Zhe Guo, Xiao-Wen Yang, Yu-Feng Li. LOG: Active Model Adaptation for Label-Efficient OOD Generalization. In: Advances in Neural Information Processing Systems (NeurIPS'22).
Jie-Jing Shao, Yunlu Xu, Zhanzhan Cheng, Yu-Feng Li. Active Model Adaptation Under Unknown Shift. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'22).
Jie-Jing Shao, Zhanzhan Cheng, Yu-Feng Li, Shiliang Pu. Towards Robust Model Reuse in the Presence of Latent Domains. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'21).
Collaborative Publications
I also work together with friends on related topics, like
Semi-Supervised Learning and
Abductive Learning.
Xiao-Wen Yang, Xuan-Yi Zhu, Wen-Da Wei, Ding-Chu Zhang, Jie-Jing Shao, Zhi Zhou, Lan-Zhe Guo, Yu-Feng Li. Step Back to Leap Forward: Self-Backtracking for Boosting Reasoning of Language Models. In: Proceedings of the 40th AAAI conference on Artificial Intelligence (AAAI'26).
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).
Wen-Da Wei, Xiao-Wen Yang, Jie-Jing Shao, Lan-Zhe Guo. Curriculum Abductive Learning for Mitigating Reasoning Shortcuts. In: Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI'25).
Lan-Zhe Guo, Lin-Han Jia, Jie-Jing Shao, Yu-Feng Li. Robust Semi-Supervised Learning in Open Environments. Frontiers of Computer Science, 2025.
Xiao-Wen Yang, Wen-Da Wei, Jie-Jing Shao, Yu-Feng Li, Zhi-Hua Zhou. Analysis for Abductive Learning and Neural-Symbolic Reasoning Shortcuts. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24).
Jiang-Xin Shi, Tong Wei, Zhi Zhou, Jie-Jing Shao, Xin-Yan Han, Yu-Feng Li. Long-Tail Learning with Foundation Model: Heavy Fine-tuning Hurts. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24).
Xiao-Wen Yang, Jie-Jing Shao, Wei-Wei Tu, Yu-Feng Li, Wang-Zhou Dai, Zhi-Hua Zhou. Safe Abductive Learning in the Presence of Inaccurate Rules. In: Proceedings of the 38th AAAI conference on Artificial Intelligence (AAAI'24).
Xiao-Wen Yang, Hong-Jie You, Peng-Xiao Song, Hao-Ran Hao, Jie-Jing Shao, Yu-Feng Li. Lightweight Retrieval Tuning for Black-Box Language Models. In: Efficient Natural Language and Speech Processing Workshop at NeurIPS'23 (ENLSP'23).
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).
Lan-Zhe Guo, Yi-Ge Zhang, Zhi-Fan Wu, Jie-Jing Shao, Yu-Feng Li. Robust Semi-Supervised Learning when Not All Classes have Labels. In: Advances in Neural Information Processing Systems (NeurIPS'22).
Lan-Zhe Guo, Zhi Zhou, Jie-Jing Shao, Qi Zhang, Feng Kuang, Gao-Le Li, Zhang-Xun Liu, Guo-Bin Wu, Nan Ma, Qun Li, Yu-Feng Li. Learning from Imbalanced and Incomplete Supervision with Its Application to Ride-Sharing Liability Judgment. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'21).
Projects
Awards & Honors
Most Innovative Approach Award, NeurIPS 2025 Challenge on Foundation Models Meet Embodied Agents, 2025.
Huawei Scholarship, Nanjing University, 2025
Best Paper Award, Robust Machine Learning in Open Environments Workshop@PAKDD 2024.
Postgraduate Research & Practice Innovation Program of Jiangsu Province, Jiangsu Province, 2024
Tencent Scholarship, Nanjing University, 2022
Outstanding Graduate Students, Nanjing University, 2021
National Scholarship, Nanjing University, 2021
LAMDA Elite Award, Runner-up. 2021
Tang Aoqing Honors Program of Research and Practice Award, First Prize, Jilin University, 2016.6
ACM-ICPC Asia Regional Contest, 2 Gold Medals, Shenyang, 2015.10 & Dalian, 2016.10
CCPC Regional Contest, Gold Medal (4th place), Changchun, 2016.9
CCPC Northeast Collegiate Programming Contest, Gold Medal (2nd place), Changchun, 2016.9
CCPC Jilin Province Collegiate Programming Contest, 2 Championships, Changchun, 2015.9 & 2016.9
Presentations
Academic Service
Co-Organizer: IJCAI 2025 Competition: Travel Planning Challenge
Senior Program Committee: IJCAI 2025.
Reviewer for Conferences:
- ICML 2023, 2024, 2025, 2026.
- NeurIPS 2023, 2024, 2025.
- ICLR 2024, 2025, 2026.
- KDD 2024, 2025 (Excellent Reviewers).
- AAAI 2023, 2024, 2025, 2026.
- IJCAI 2024, 2025.
- ECAI 2023; ACML 2021, 2022.
Reviewer for Journals:
- Machine Learning.
- Neural Networks.
- Journal of Artificial Intelligence Research.
- IEEE Transactions on Artificial Intelligence.
Social Service
Teaching Assistant, Advanced Machine Learning (NJU, For Graduate Students), Spring, 2021
Teaching Assistant, Introduction of Machine Learning (NJU, For Undergraduate Students), Fall, 2020
Student Coach, Jilin University ACM-ICPC Team, 2015-2019
Correspondence
Email: shaojj@lamda.nju.edu.cn or shjjhszs@gmail.com
Address: Jie-Jing Shao, National Key Laboratory for Novel Software Technology, Nanjing University, 163 Xianlin Avenue, Qixia District, Nanjing 210023, China.
(南京市栖霞区仙林大道163号, 南京大学仙林校区, 软件新技术国家重点实验室, 210023.)
邵杰晶