| Learning loop invariants for program verification X Si, H Dai, M Raghothaman, M Naik, L Song Advances in Neural Information Processing Systems 31, 2018 | 183 | 2018 |
| APISan: Sanitizing API Usages through Semantic Cross-checking I Yun, C Min, X Si, Y Jang, T Kim, M Naik 25th USENIX Security Symposium (USENIX Security 16), 363-378, 2016 | 96 | 2016 |
| Scallop: From probabilistic deductive databases to scalable differentiable reasoning J Huang, Z Li, B Chen, K Samel, M Naik, L Song, X Si Advances in Neural Information Processing Systems 34, 25134-25145, 2021 | 81 | 2021 |
| Syntax-guided synthesis of datalog programs X Si, W Lee, R Zhang, A Albarghouthi, P Koutris, M Naik Proceedings of the 2018 26th ACM Joint Meeting on European Software …, 2018 | 79 | 2018 |
| Synthesizing datalog programs using numerical relaxation X Si, M Raghothaman, K Heo, M Naik arXiv preprint arXiv:1906.00163, 2019 | 72 | 2019 |
| Code2inv: A deep learning framework for program verification X Si, A Naik, H Dai, M Naik, L Song Computer Aided Verification: 32nd International Conference, CAV 2020, Los …, 2020 | 59 | 2020 |
| Learning a meta-solver for syntax-guided program synthesis X Si, Y Yang, H Dai, M Naik, L Song International Conference on Learning Representations, 2018 | 42 | 2018 |
| Effective interactive resolution of static analysis alarms X Zhang, R Grigore, X Si, M Naik Proceedings of the ACM on Programming Languages 1 (OOPSLA), 1-30, 2017 | 34 | 2017 |
| Can chatgpt pass an introductory level functional language programming course? C Geng, Y Zhang, B Pientka, X Si arXiv preprint arXiv:2305.02230, 2023 | 28 | 2023 |
| Techniques for symbol grounding with satnet S Topan, D Rolnick, X Si Advances in Neural Information Processing Systems 34, 20733-20744, 2021 | 26 | 2021 |
| Continuously reasoning about programs using differential bayesian inference K Heo, M Raghothaman, X Si, M Naik Proceedings of the 40th ACM SIGPLAN Conference on Programming Language …, 2019 | 26 | 2019 |
| Automated black-box detection of access control vulnerabilities in web applications X Li, X Si, Y Xue Proceedings of the 4th ACM Conference on Data and Application Security and …, 2014 | 26 | 2014 |
| Maximum satisfiability in software analysis: Applications and techniques X Si, X Zhang, R Grigore, M Naik International Conference on Computer Aided Verification, 68-94, 2017 | 23 | 2017 |
| Towards reliable neural specifications C Geng, N Le, X Xu, Z Wang, A Gurfinkel, X Si International Conference on Machine Learning, 11196-11212, 2023 | 18 | 2023 |
| Parallel optimization of queries in XML dataset using GPU X Si, A Yin, X Huang, X Yuan, X Liu, G Wang 2011 Fourth International Symposium on Parallel Architectures, Algorithms …, 2011 | 12 | 2011 |
| Combining the logical and the probabilistic in program analysis X Zhang, X Si, M Naik Proceedings of the 1st ACM SIGPLAN International Workshop on Machine …, 2017 | 11 | 2017 |
| A Survey on Deep Learning for Theorem Proving Z Li, J Sun, L Murphy, Q Su, Z Li, X Zhang, K Yang, X Si arXiv preprint arXiv:2404.09939, 2024 | 10 | 2024 |
| NSNet: A general neural probabilistic framework for satisfiability problems Z Li, X Si Advances in Neural Information Processing Systems 35, 25573-25585, 2022 | 10 | 2022 |
| On incremental core-guided MaxSAT solving X Si, X Zhang, V Manquinho, M Janota, A Ignatiev, M Naik Principles and Practice of Constraint Programming: 22nd International …, 2016 | 10 | 2016 |
| Autoformalizing Euclidean Geometry L Murphy, K Yang, J Sun, Z Li, A Anandkumar, X Si arXiv preprint arXiv:2405.17216, 2024 | 6 | 2024 |
Mayur NaikMisra Family Professor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu