Published in NeurIPS, 2026
Recommended citation: Geng, C., Zhang, L., Zhang, M., Wang, Z., Ye, H., & Si, X. Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generative Modeling. In Advances in Neural Information Processing Systems (NeurIPS), 2026. (accepted) https://arxiv.org/pdf/2602.16954
Published in ICLR, 2026
Recommended citation: Geng, C., Zhao, Z., Wang, Z., Ye, H., Jiang, Y., & Si, X. LogicXGNN: Grounded Logical Rules for Explaining Graph Neural Networks. In International Conference on Learning Representations (ICLR), 2026. https://openreview.net/pdf?id=nqZe43tRY9
Published in NeuS (Oral), 2025
Recommended citation: Geng, C., Wang, Z., Ye, H., & Si, X. Learning Minimal Neural Specifications. NeuS 2025 (Oral). https://proceedings.mlr.press/v288/geng25a.html
Published in arXiv preprint, 2024
Recommended citation: Geng, C., Ye, H., Zhang, Y., Pientka, B., & Si, X. SAT-DIFF: A Tree Diffing Framework Using SAT Solving. arXiv preprint arXiv:2404.04731, 2024. https://arxiv.org/pdf/2404.04731
Published in SRC@SPLASH, 2023
Recommended citation: Ye, H. (2023). An Optimal Structure-Aware Code Difference Framework with MaxSAT-Solver. SPLASH Companion’23, 43.
Published in NeurReps@NeurIPS 2023; PMLR 2024, 2023
Recommended citation: Geng, C., Xu, X., Ye, H., & Si, X. Scalar Invariant Networks with Zero Bias. NeurIPS 2023 Workshop on Symmetry and Geometry in Neural Representations; PMLR, 2024. https://proceedings.mlr.press/v228/geng24a.html
Published in APLAS, 2022
Recommended citation: Geng, C., Ye, H., Li, Y., Han, T., Pientka, B., & Si, X. (2022, November). Novice Type Error Diagnosis with Natural Language Models. In Asian Symposium on Programming Languages and Systems (pp. 196-214). Cham: Springer Nature Switzerland.