胡文超 Wen-Chao Hu Ph.D. Candidate, LAMDA Group School of Artificial Intelligence Nanjing University, Nanjing 210023, China Supervisor: Prof. Yuan Jiang and Zhi-Hua Zhou Email: huwc@lamda.nju.edu.cn [CV]

Biography

I am currently pursuing my Ph.D. degree in LAMDA Group, School of Artificial Intelligence, Nanjing University, where I have the great privilege of being advised by Prof. Yuan Jiang and Prof. Zhi-Hua Zhou. Prior to that, I received my B.Eng. degree in June 2021 from School of Intelligent Systems Engineering, Sun Yat-sen University.

My research interests focus on advancing reliable and efficient AI methods, particularly in the following areas:

• Abductive Learning and Neuro-Symbolic Methods: Integrating the power of data-driven AI (e.g., machine learning, LLMs) and knowledge-driven AI (e.g., logical reasoning) to build AI systems that are logically reliable and suitable for high-stakes applications.
• Optimization: Developing efficient algorithms for joint optimization involving both continuous numerical variables and discrete symbolic structures, a core challenge in computer science (including but not limited to neuro-symbolic integration).

Publication

Conference Paper

• Curriculum Abductive Learning. [arXiv, bibtex]
  Wen-Chao Hu, Qi-Jie Li, Lin-Han Jia, Cunjing Ge, Yu-Feng Li, Yuan Jiang and Zhi-Hua Zhou.
  In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), San Diego, CA, 2025. Page: to appear. CCF-A
  

  We enhance ABL efficiency, stability, and reasoning accuracy by controlling the knowledge base complexity throughout training.




• Verification Learning: Make Unsupervised Neuro-Symbolic System Feasible. [PDF, arXiv, bibtex]
  Lin-Han Jia, Wen-Chao Hu, Jie-Jing Shao, Lan-Zhe Guo, and Yu-Feng Li.
  In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), Vancouver, Canada, 2025. Page: to appear. CCF-A
  

  We propose a label-free neuro-symbolic method by verification, which is formulated as a COP and solved via DCS.




• Efficient Rectification of Neuro-Symbolic Reasoning Inconsistencies by Abductive Reflection. [PDF, arXiv, poster, code, bibtex]
  (Outstanding Paper Award)
  Wen-Chao Hu, Wang-Zhou Dai, Yuan Jiang, and Zhi-Hua Zhou.
  In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25), Philadelphia, PA, 2025. Page: 17333-17341. CCF-A
  

  ♣ Extended abstract version [PDF], published in: Proceedings of the 34th International Joint Conference on Artificial Intelligence
  (IJCAI'25 Best Papers from Sister Conferences Track), Montreal, Canada, 2025. Page: 10896-10900. CCF-A
  

  We propose an efficient ABL extension combining neural networks and symbolic reasoning with both sides' integrity preserved.




• Knowledge-Enhanced Historical Document Segmentation and Recognition. [PDF, bibtex]  (Oral)
  En-Hao Gao, Yu-Xuan Huang, Wen-Chao Hu, Xin-Hao Zhu, and Wang-Zhou Dai.
  In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), Vancouver, Canada, 2024. Page: 8409--8416. CCF-A
  

  We apply ABL to enhance historical document OCR through effective use of formal expert knowledge.

Journal Paper

• ABLkit: A Python Toolkit for Abductive Learning. [PDF, bibtex]
  Yu-Xuan Huang, Wen-Chao Hu, En-Hao Gao, and Yuan Jiang.
  Frontiers of Computer Science, 2024, 18(6):186354. CCF-B
  

Preprint

• When Is Prior Knowledge Helpful? Exploring the Evaluation and Selection of Unsupervised Pretext Tasks from a Neuro-Symbolic Perspective. [arXiv, bibtex]
  Lin-Han Jia, Si-Yu Han, Wen-Chao Hu, Jie-Jing Shao, Wen-Da Wei, Zhi Zhou, Lan-Zhe Guo, and Yu-Feng Li.

Projects

• ABLkit [GitHub, PyPI]: I am the core developer and current maintainer of ABLkit, an efficient open-source toolkit for abductive learning, which leverages the power of both data and knowledge. For more information, please refer to: ABLkit Documentation.

Awards

• Outstanding Paper Award of AAAI 2025. (3 outstanding papers out of 12,957 submissions) [certificate, photo]

• Second Prize (3rd place), Greater Bay Area International Algorithm Competition, 2022. (¥100,000 award)

• Outstanding Bachelor's Thesis of Sun Yat-sen University, 2021.

Teaching Assistant

• Digital Signal Processing (with Prof. Wei Wang; 2022 fall, 2023 fall, 2025 spring, 2025 fall)

• Introduction to Machine Learning (with Prof. Zhi-Hua Zhou; 2022 fall, 2024 spring)

• Mathematical Logic (with Dr. Cunjing Ge; 2023 spring, 2024 spring)

• Symbolic Learning (with Assoc. Prof. Wang-Zhou Dai; 2022 spring)

• Operating Systems (with Dr. Xintao Niu and Huayao Wu; 2022 spring)

Academic Service

• PC Member or Reviewer for Conferences: NeurIPS (2024, 2025), NeSy (2024, 2025), IJCLR (2024), ICLR (2025, 2026), AISTATS (2025, 2026), ICML (2025), IJCAI (2025), PAKDD (2026).

• Volunteer for Conferences: MLA (2023), IJCLR (2024).

• Server Administrator for LAMDA group, 2021-2022.

Miscellaneous

• We hold a weekly Abductive Learning (ABL) Seminar. I serve as the seminar organizer and website maintainer. If you are interested in joining, feel free to contact me.

• I was once actively involved in British Parliamentary-style debate, winning the championship at the 2017 Yat-sen Open, Guangzhou and reaching the quarter-finals at the 2018 Asian Debate Open, Seoul.

• I am an enthusiast of strength training (fitness/CrossFit) and road bycicle racing, see photos here.

Correspondence

Email:
huwc@lamda.nju.edu.cn

Office:
Room 912, Computer Science Building, Xianlin Campus of Nanjing University

Mail Address:
National Key Laboratory for Novel Software Technology, Nanjing University, Xianlin Campus, 163 Xianlin Avenue, Qixia District, Nanjing 210023, China
(江苏省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)