Publications

Selected Publications

Thanks to all the collaborators I’ve worked with. ☺

  1. Li, Q., Xia, W., Dai, X., Du, K., Liu, W., Wang, Y., … & Zhang, W. (2025, November). Rethinkmcts: Refining erroneous thoughts in monte carlo tree search for code generation. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (pp. 8103-8121).
  2. Li, X., Dong, K., Lee, Y. Q., Xia, W., Zhang, H., Dai, X., … & Tang, R. (2025, July). Coir: A comprehensive benchmark for code information retrieval models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 22074-22091).
  3. Lin, Y., Chen, H., Xia, W., Lin, F., Wang, Z., & Liu, Y. (2025). A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining: Y. Lin et al. Data Science and Engineering, 1-27.
  4. Fu, L., Long, T., Lin, J., Xia, W., Dai, X., Tang, R., … & Yu, Y. (2025, September). AdvKT: An Adversarial Multi-step Training Framework for Knowledge Tracing. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 183-200). Cham: Springer Nature Switzerland.
  5. Long, T., Yin, L. A., Chang, Y., Xia, W., & Yu, Y. (2025, April). Simulating Question-answering Correctness with a Conditional Diffusion. In Proceedings of the ACM on Web Conference 2025 (pp. 5173-5182).
  6. Fu, L., Guan, H., Du, K., Lin, J., Xia, W., Zhang, W., … & Yu, Y. (2024, October). Sinkt: A structure-aware inductive knowledge tracing model with large language model. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (pp. 632-642).
  7. Li, Q., Xia, W., Yin, L. A., Jin, J., & Yu, Y. (2024, August). Privileged Knowledge State Distillation for Reinforcement Learning-based Educational Path Recommendation. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 1621-1630).
  8. Liu, Y., Xia, W.*, Liu, W., Zhu, M., Zhang, W., Tang, R., & Yu, Y. (2024, May). HiFI: Hierarchical Fairness-aware Integrated Ranking with Constrained Reinforcement Learning. In Companion Proceedings of the ACM on Web Conference 24(pp. 196-205).
  9. Li, Q., Xia, W., Yin, L. A., Shen, J., Rui, R., Zhang, W., … & Yu, Y. (2023, October). Graph Enhanced Hierarchical Reinforcement Learning for Goal-oriented Learning Path Recommendation. In Proceedings of the 32nd ACM CIKM 23 (pp. 1318-1327).
  10. Wang, H., Long, T., Yin, L., Zhang, W., Xia, W., Hong, Q., … & Yu, Y. (2023, August). GMOCAT: A Graph-Enhanced Multi-Objective Method for Computerized Adaptive Testing. In Proceedings of the 29th ACM SIGKDD 23 (pp. 2279-2289).
  11. Zhu, M., Xia, W.*, Liu, W., Liu, Y., Tang, R., & Zhang, W. (2023, April). Integrated Ranking for News Feed with Reinforcement Learning. In Companion Proceedings of the ACM Web Conference 23 (pp. 480-484).
  12. Chen, X., Shen, J., Xia, W., Jin, J., Song, Y., Zhang, W., … & Yu, Y. (2023, February). Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation. AAAI 23 (pp. 5027–5035).
  13. Pan, L., Qian, J., Xia, W., Mao, H., Yao, J., Li, P., & Xiao, Z. (2022, November). Optimizing communication in deep reinforcement learning with XingTian. In Proceedings of the 23rd ACM/IFIP International Middleware Conference (pp. 255-268).
  14. Xia, W., Liu, W., Liu, Y., & Tang, R. (2022, October). Balancing Utility and Exposure Fairness for Integrated Ranking with Reinforcement Learning. In Proceedings of the 31st ACM CIKM 2022 (pp. 4590-4594).
  15. He, Z., Xia, W.*, Dong, K., Guo, H., Tang, R., Xia, D., & Zhang, R. (2022, August). Unsupervised learning style classification for learning path generation in online education platforms. In Proceedings of the 28th ACM SIGKDD 22 (pp. 2997-3006).
  16. Long, T., Qin, J., Shen, J., Zhang, W., Xia, W., Tang, R., … & Yu, Y. (2022, February). Improving knowledge tracing with collaborative information. In Proceedings of the fifteenth ACM WSDM 22 (pp. 599-607).
  17. Xia, W., Li, H., & Li, B. (2016, December). A control strategy of autonomous vehicles based on deep reinforcement learning. In 2016 9th International Symposium on Computational Intelligence and Design (ISCID) (Vol. 2, pp. 198-201). IEEE.
  18. 夏伟, 李慧云. 基于深度强化学习的自动驾驶策略学习方法[J]. 集成技术, 2017(3). 于2023年入选《学术精要数据库》近10年的“三高”论文（高PCSI、高引用和高下载，top 1%）