| Hoppity: Learning graph transformations to detect and fix bugs in programs E Dinella, H Dai, Z Li, M Naik, L Song, K Wang International Conference on Learning Representations (ICLR), 2020 | 221 | 2020 |
| 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 | 47 | 2021 |
| Arbitrar: User-guided api misuse detection Z Li, A Machiry, B Chen, M Naik, K Wang, L Song 2021 IEEE Symposium on Security and Privacy (SP), 1400-1415, 2021 | 21 | 2021 |
| Improved logical reasoning of language models via differentiable symbolic programming H Zhang, J Huang, Z Li, M Naik, E Xing arXiv preprint arXiv:2305.03742, 2023 | 18 | 2023 |
| Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities A Khare, S Dutta, Z Li, A Solko-Breslin, R Alur, M Naik arXiv preprint arXiv:2311.16169, 2023 | 7 | 2023 |
| Scallop: A language for neurosymbolic programming Z Li, J Huang, M Naik Proceedings of the ACM on Programming Languages 7 (PLDI), 1463-1487, 2023 | 6 | 2023 |
| Laser: Neuro-symbolic learning of semantic video representations J Huang, Z Li, D Jacobs, M Naik, SN Lim arXiv preprint arXiv:2304.07647, 2023 | 3 | 2023 |
| Relational Programming with Foundational Models Z Li, J Huang, J Liu, F Zhu, E Zhao, W Dodds, N Velingker, R Alur, M Naik Proceedings of the AAAI Conference on Artificial Intelligence 38 (9), 10635 …, 2024 | | 2024 |
| DISCRET: a self-interpretable framework for treatment effect estimation Y Wu, N Velingker, Z Li, K Chen, M Keoliya, M Naik, Q Long, E Wong, ... | | 2023 |
| Beyond Differentiability: Neurosymbolic Learning with Black-Box Programs A Solko-Breslin, Z Li, N Velingker, R Alur, M Naik | | 2023 |
Mayur NaikProfessor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu
