Jie-Jing Shao @ LAMDA, NJU-CS

(* denotes equal contributions)

• 
  ChinaTravel: An Open-Ended Travel Planning Benchmark with Compositional Constraint Validation for Language Agents.
  Jie-Jing Shao*, Bo-Wen Zhang*, Xiao-Wen Yang*, Bai-Zhi Chen, Si-Yu Han, Jing-Hao Pang, Wen-Da Wei, Guohao Cai, Zhenhua Dong, Lan-Zhe Guo, Yu-Feng Li.
  

  ICLR 2026 Paper Project Page Code Dataset

  Based on this benchmark, we organize the The IJCAI-25 Travel Planning Challenge and the AIC 2025 Travel Planning Challenge.
  The developed demo was honored with the Excellence Award at the National "AI+" Industry Application Innovation Competition.
• 
  Neuro-Symbolic Artificial Intelligence: Towards Improving the Reasoning Abilities of Large Language Models.
  Xiao-Wen Yang*, Jie-Jing Shao*, Lan-Zhe Guo*, Bo-Wen Zhang, Zhi Zhou, Lin-Han Jia, Wang-Zhou Dai, Yu-Feng Li.
  

  IJCAI 2025 Survey Track Paper Project Page

  Highlighted in a Nature News Feature article as one of ten representative papers in neuro-symbolic research [Link].
• 
  Abductive Learning for Neuro-Symbolic Grounded Imitation.
  Jie-Jing Shao*, Hao-Ran Hao*, Xiao-Wen Yang, Yu-Feng Li.
  

  KDD 2025 Paper Project Page Code

  PAKDD 2024 Workshop on Robust Machine Learning in Open Environments, Best Paper Award (Slides)
• 
  Offline Imitation Learning with Model-based Reverse Augmentation.
  Jie-Jing Shao*, Hao-Sen Shi*, Lan-Zhe Guo, Yu-Feng Li.
  

  KDD 2024 Paper Project Page Code Slides
• 
  Analysis for Abductive Learning and Neural-Symbolic Reasoning Shortcuts.
  Xiao-Wen Yang, Wen-Da Wei, Jie-Jing Shao, Yu-Feng Li, Zhi-Hua Zhou.
  

  ICML 2024 Paper Project Page
• 
  Open-Set Learning under Covariate Shift.
  Jie-Jing Shao*, Xiao-Wen Yang*, Lan-Zhe Guo.
  

  Machine Learning 2024 Paper
• 
  LOG: Active Model Adaptation for Label-Efficient OOD Generalization.
  Jie-Jing Shao, Lan-Zhe Guo, Xiao-Wen Yang, Yu-Feng Li.
  

  NeurIPS 2022 Paper
• 
  Active Model Adaptation Under Unknown Shift.
  Jie-Jing Shao, Yunlu Xu, Zhanzhan Cheng, Yu-Feng Li.
  

  KDD 2022 Paper

(* denotes equal contributions)

• ChinaTravel: An Open-Ended Travel Planning Benchmark with Compositional Constraint Validation for Language Agents.
  Jie-Jing Shao*, Bo-Wen Zhang*, Xiao-Wen Yang*, Bai-Zhi Chen, Si-Yu Han, Jing-Hao Pang, Wen-Da Wei, Guohao Cai, Zhenhua Dong, Lan-Zhe Guo, Yu-Feng Li.
  
  ICLR 2026 Paper Project Page Code Dataset
  Based on this benchmark, we organize a competition on IJCAI 2025: The IJCAI-25 Travel Planning Challenge .
  We also organize a competition with JSAI on AIC (Global Campus Artificial Intelligence Algorithm Elite Competition): The AIC 2025 Travel Planning Challenge .
• Neuro-Symbolic Artificial Intelligence: Towards Improving the Reasoning Abilities of Large Language Models.
  Xiao-Wen Yang*, Jie-Jing Shao*, Lan-Zhe Guo*, Bo-Wen Zhang, Zhi Zhou, Lin-Han Jia, Wang-Zhou Dai, Yu-Feng Li.
  
  IJCAI 2025 Survey Track Paper Github Repo
• Breaking the Self-Evaluation Barrier: Reinforced Neuro-Symbolic Planning with Large Language Models.
  Jie-Jing Shao*, Hong-Jie You*, Guohao Cai, Quanyu Dai, Zhenhua Dong, Lan-Zhe Guo.
  
  IJCAI 2025 Paper
• Abductive Learning for Neuro-Symbolic Grounded Imitation.
  Jie-Jing Shao*, Hao-Ran Hao*, Xiao-Wen Yang, Yu-Feng Li.
  
  KDD 2025 Paper Webpage Code
  PAKDD 2024 Workshop on Robust Machine Learning in Open Environments, Best Paper Award (Slides)
• Offline Imitation Learning with Model-based Reverse Augmentation.
  Jie-Jing Shao*, Hao-Sen Shi*, Lan-Zhe Guo, Yu-Feng Li.
  
  KDD 2024 Paper Webpage Code Slides
• Open-Set Learning under Covariate Shift.
  Jie-Jing Shao*, Xiao-Wen Yang*, Lan-Zhe Guo.
  
  Machine Learning Paper
• Offline Imitation Learning without Auxiliary High-quality Behavior Data.
  Jie-Jing Shao, Hao-Sen Shi, Tian Xu, Lan-Zhe Guo, Yang Yu, Yu-Feng Li.
  
  Preprint 2023 Paper
• Investigating the Limitation of CLIP Models: The Worst-performing Categories.
  Jie-Jing Shao*, Jiang-Xin Shi*, Xiao-Wen Yang*, Lan-Zhe Guo, Yu-Feng Li.
  
  Preprint 2023 Paper
• LOG: Active Model Adaptation for Label-Efficient OOD Generalization.
  Jie-Jing Shao, Lan-Zhe Guo, Xiao-Wen Yang, Yu-Feng Li.
  
  NeurIPS 2022 Paper
• Active Model Adaptation Under Unknown Shift.
  Jie-Jing Shao, Yunlu Xu, Zhanzhan Cheng, Yu-Feng Li.
  
  KDD 2022 Paper
• Towards Robust Model Reuse in the Presence of Latent Domains.
  Jie-Jing Shao, Zhanzhan Cheng, Yu-Feng Li, Shiliang Pu.
  
  IJCAI 2021 Paper

• Jie-Jing Shao*, Bo-Wen Zhang*, Xiao-Wen Yang*, Bai-Zhi Chen, Si-Yu Han, Jing-Hao Pang, Wen-Da Wei, Guohao Cai, Zhenhua Dong, Lan-Zhe Guo, Yu-Feng Li. ChinaTravel: An Open-Ended Travel Planning Benchmark with Compositional Constraint Validation for Language Agents. In: Proceedings of The 14th International Conference on Learning Representations (ICLR'26).
  The preliminary version is accepted on the LLM Evals Workshop @ NeurIPS 2025. (Webpage, Code)
• 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).

• 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*, 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).
• 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.

• 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*, Xiao-Wen Yang*, Lan-Zhe Guo. Open-Set Learning under Covariate Shift. Machine Learning, 2024.
• 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).

• 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.
• 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).

• 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).
• 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).
• 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).
• 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).

• Jie-Jing Shao*, Bo-Wen Zhang*, Xiao-Wen Yang*, Bai-Zhi Chen, Si-Yu Han, Jing-Hao Pang, Wen-Da Wei, Guohao Cai, Zhenhua Dong, Lan-Zhe Guo, Yu-Feng Li. ChinaTravel: An Open-Ended Travel Planning Benchmark with Compositional Constraint Validation for Language Agents. In: Proceedings of The 14th International Conference on Learning Representations (ICLR'26).
  The preliminary version is accepted on the LLM Evals Workshop @ NeurIPS 2025. (Webpage, Code)
• 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).
• 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, 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).

• 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)
• 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).
• 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).
• 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).

• Lan-Zhe Guo, Lin-Han Jia, Jie-Jing Shao, Yu-Feng Li. Robust Semi-Supervised Learning in Open Environments. Frontiers of Computer Science, 2025.
• 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*, Xiao-Wen Yang*, Lan-Zhe Guo. Open-Set Learning under Covariate Shift. Machine Learning, 2024.
• 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).
• 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.
• 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).
• 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).
• 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).
• 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).
• 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).