| A user-guided approach to program analysis R Mangal, X Zhang, AV Nori, M Naik Proceedings of the 2015 10th Joint Meeting on Foundations of Software …, 2015 | 108 | 2015 |
| On abstraction refinement for program analyses in Datalog X Zhang, R Mangal, R Grigore, M Naik, H Yang Proceedings of the 35th ACM SIGPLAN Conference on Programming Language …, 2014 | 103 | 2014 |
| Robustness of neural networks: A probabilistic and practical approach R Mangal, AV Nori, A Orso 2019 IEEE/ACM 41st International Conference on Software Engineering: New …, 2019 | 92 | 2019 |
| Hybrid top-down and bottom-up interprocedural analysis X Zhang, R Mangal, M Naik, H Yang Proceedings of the 35th acm sigplan conference on programming language …, 2014 | 48 | 2014 |
| Closed-loop analysis of vision-based autonomous systems: A case study CS Păsăreanu, R Mangal, D Gopinath, S Getir Yaman, C Imrie, ... International conference on computer aided verification, 289-303, 2023 | 36 | 2023 |
| Accelerating program analyses by cross-program training S Kulkarni, R Mangal, X Zhang, M Naik ACM SIGPLAN Notices 51 (10), 359-377, 2016 | 26 | 2016 |
| Controller synthesis for autonomous systems with deep-learning perception components R Calinescu, C Imrie, R Mangal, GN Rodrigues, C Păsăreanu, ... IEEE Transactions on Software Engineering, 2024 | 21* | 2024 |
| A correspondence between two approaches to interprocedural analysis in the presence of join R Mangal, M Naik, H Yang European Symposium on Programming Languages and Systems, 513-533, 2014 | 20 | 2014 |
| Probabilistic Lipschitz analysis of neural networks R Mangal, K Sarangmath, AV Nori, A Orso Static Analysis: 27th International Symposium, SAS 2020, Virtual Event …, 2020 | 14 | 2020 |
| Self-correcting Neural Networks for Safe Classification K Leino, A Fromherz, R Mangal, M Fredrikson, B Parno, C Păsăreanu Software Verification and Formal Methods for ML-Enabled Autonomous Systems …, 2022 | 12* | 2022 |
| Scaling relational inference using proofs and refutations R Mangal, X Zhang, A Kamath, A Nori, M Naik Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 12 | 2016 |
| Volt: A lazy grounding framework for solving very large MaxSAT instances R Mangal, X Zhang, AV Nori, M Naik International Conference on Theory and Applications of Satisfiability …, 2015 | 12 | 2015 |
| Query-guided maximum satisfiability X Zhang, R Mangal, AV Nori, M Naik Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of …, 2016 | 10 | 2016 |
| Attacks and Defenses for Large Language Models on Coding Tasks C Zhang, Z Wang, R Zhao, R Mangal, M Fredrikson, L Jia, C Pasareanu Proceedings of the 39th IEEE/ACM International Conference on Automated …, 2024 | 5* | 2024 |
| Assumption generation for learning-enabled autonomous systems CS Păsăreanu, R Mangal, D Gopinath, H Yu International Conference on Runtime Verification, 3-22, 2023 | 5* | 2023 |
| Concept-based analysis of neural networks via vision-language models R Mangal, N Narodytska, D Gopinath, BC Hu, A Roy, S Jha, ... International Symposium on AI Verification, 49-77, 2024 | 2 | 2024 |
| Is Certifying Robustness Still Worthwhile? R Mangal, K Leino, Z Wang, K Hu, W Yu, C Pasareanu, A Datta, ... arXiv preprint arXiv:2310.09361, 2023 | 2 | 2023 |
| Feature-Guided Analysis of Neural Networks. D Gopinath, L Lungeanu, R Mangal, CS Pasareanu, S Xie, H Yu FASE, 133-142, 2023 | 2 | 2023 |
| Degradation Attacks on Certifiably Robust Neural Networks K Leino, C Zhang, R Mangal, M Fredrikson, B Parno, C Pasareanu Transactions of Machine Learning Research, 2022 | 2 | 2022 |
| Creating an interprocedural analyst-oriented data flow representation for binary analysts (CIAO) MA Leger, KM Butler, D Bueno, M Crepeau, C Cuellar, MJ Haas, ... Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2018 | 2 | 2018 |
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