The marabou framework for verification and analysis of deep neural networks G Katz, DA Huang, D Ibeling, K Julian, C Lazarus, R Lim, P Shah, ... Computer Aided Verification: 31st International Conference, CAV 2019, New …, 2019 | 592 | 2019 |

An SMT-Based Approach for Verifying Binarized Neural Networks G Amir, H Wu, C Barrett, G Katz Tools and Algorithms for the Construction and Analysis of Systems, 203-222, 2021 | 65 | 2021 |

Parallelization techniques for verifying neural networks H Wu, A Ozdemir, A Zeljić, K Julian, A Irfan, D Gopinath, S Fouladi, G Katz, ... 2020 Formal Methods in Computer Aided Design (FMCAD), 128-137, 2020 | 63 | 2020 |

G2SAT: Learning to Generate SAT Formulas J You, H Wu, C Barrett, R Ramanujan, J Leskovec Advances in neural information processing systems, 10553-10564, 2019 | 39 | 2019 |

Efficient neural network analysis with sum-of-infeasibilities H Wu, A Zeljić, G Katz, C Barrett International Conference on Tools and Algorithms for the Construction and …, 2022 | 35 | 2022 |

Global optimization of objective functions represented by ReLU networks CA Strong, H Wu, A Zeljić, KD Julian, G Katz, C Barrett, MJ Kochenderfer Machine Learning, 2021 | 35 | 2021 |

Toward certified robustness against real-world distribution shifts H Wu, T Tagomori, A Robey, F Yang, N Matni, G Pappas, H Hassani, ... 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 537-553, 2023 | 19 | 2023 |

Deepcert: Verification of contextually relevant robustness for neural network image classifiers C Paterson, H Wu, J Grese, R Calinescu, CS Păsăreanu, C Barrett Computer Safety, Reliability, and Security: 40th International Conference …, 2021 | 18 | 2021 |

Improving sat-solving with machine learning H Wu Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science …, 2017 | 18 | 2017 |

Towards verification of neural networks for small unmanned aircraft collision avoidance A Irfan, KD Julian, H Wu, C Barrett, MJ Kochenderfer, B Meng, J Lopez 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC), 1-10, 2020 | 17 | 2020 |

On reducing over-approximation errors for neural network verification T Zelazny, H Wu, C Barrett, G Katz Proc. 22nd Int. Conf. on Formal Methods in Computer-Aided Design (FMCAD), 17-26, 2022 | 13* | 2022 |

Scalable verification of GNN-based job schedulers H Wu, C Barrett, M Sharif, N Narodytska, G Singh Proceedings of the ACM on Programming Languages 6 (OOPSLA2), 1036-1065, 2022 | 10 | 2022 |

Verix: Towards verified explainability of deep neural networks M Wu, H Wu, C Barrett Advances in neural information processing systems 36, 2024 | 9 | 2024 |

Learning to generate industrial sat instances H Wu, R Ramanujan Proceedings of the International Symposium on Combinatorial Search 10 (1 …, 2019 | 8 | 2019 |

Marabou 2.0: A Versatile Formal Analyzer of Neural Networks H Wu, O Isac, A Zeljić, T Tagomori, M Daggitt, W Kokke, I Refaeli, G Amir, ... arXiv preprint arXiv:2401.14461, 2024 | 6 | 2024 |

Convex bounds on the softmax function with applications to robustness verification D Wei, H Wu, M Wu, PY Chen, C Barrett, E Farchi International Conference on Artificial Intelligence and Statistics, 6853-6878, 2023 | 6 | 2023 |

Lemur: Integrating Large Language Models in Automated Program Verification H Wu, C Barrett, N Narodytska International Conference on Learning Representations, 2024 | 5 | 2024 |

Towards Efficient Verification of Quantized Neural Networks P Huang, H Wu, Y Yang, I Daukantas, M Wu, Y Zhang, C Barrett Proceedings of the AAAI Conference on Artificial Intelligence 38 (19), 21152 …, 2024 | 4 | 2024 |

Sat solving in the serverless cloud A Ozdemir, H Wu, C Barrett 2021 Formal Methods in Computer Aided Design (FMCAD), 241-245, 2021 | 4 | 2021 |

Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates U Mandal, G Amir, H Wu, I Daukantas, FL Newell, UJ Ravaioli, B Meng, ... arXiv preprint arXiv:2405.14058, 2024 | 3 | 2024 |