Improving SAT-solving with Machine Learning. - Haoze Wu 001 (SIGCSE, 2017)

Improve SAT-solving with Machine Learning. - Haoze Wu 001 (CoRR, 2017)

The Marabou Framework for Verification and Analysis of Deep Neural Networks. - Guy Katz; Derek A. Huang; Duligur Ibeling; Kyle Julian; Christopher Lazarus; Rachel Lim; Parth Shah 003; Shantanu Thakoor; Haoze Wu 001; Aleksandar Zeljic; David L. Dill; Mykel J. Kochenderfer; et al. (CAV (1), 2019)

G2SAT: Learning to Generate SAT Formulas. - Jiaxuan You; Haoze Wu 001; Clark W. Barrett; Raghuram Ramanujan; Jure Leskovec (NeurIPS, 2019)

Learning to Generate Industrial SAT Instances. - Haoze Wu 001; Raghuram Ramanujan (SOCS, 2019)

Parallelization Techniques for Verifying Neural Networks. - Haoze Wu 001; Alex Ozdemir; Aleksandar Zeljic; Kyle Julian; Ahmed Irfan; Divya Gopinath; Sadjad Fouladi; Guy Katz; Corina S. Pasareanu; Clark W. Barrett (FMCAD, 2020)

SAT Solving in the Serverless Cloud. - Alex Ozdemir; Haoze Wu 001; Clark W. Barrett (FMCAD, 2021)

DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers. - Colin Paterson; Haoze Wu 001; John Grese; Radu Calinescu; Corina S. Pasareanu; Clark W. Barrett (SAFECOMP, 2021)

An SMT-Based Approach for Verifying Binarized Neural Networks. - Guy Amir; Haoze Wu 001; Clark W. Barrett; Guy Katz (TACAS (2), 2021)

Proof-Stitch: Proof Combination for Divide-and-Conquer SAT Solvers. - Abhishek Anil Nair; Saranyu Chattopadhyay; Haoze Wu 001; Alex Ozdemir; Clark W. Barrett (FMCAD, 2022)

On Optimizing Back-Substitution Methods for Neural Network Verification. - Tom Zelazny; Haoze Wu 001; Clark W. Barrett; Guy Katz (FMCAD, 2022)

Efficient Neural Network Analysis with Sum-of-Infeasibilities. - Haoze Wu 001; Aleksandar Zeljic; Guy Katz; Clark W. Barrett (TACAS (1), 2022)

Proof-Stitch: Proof Combination for Divide and Conquer SAT Solvers. - Abhishek Nair; Saranyu Chattopadhyay; Haoze Wu 001; Alex Ozdemir; Clark W. Barrett (CoRR, 2022)

Scalable verification of GNN-based job schedulers. - Haoze Wu 001; Clark W. Barrett; Mahmood Sharif; Nina Narodytska; Gagandeep Singh 001 (Proc. ACM Program. Lang., 2022)

Artifact for Paper Efficient Neural Network Analysis with Sum-of-Infeasibilities. - Haoze Wu 001; Aleksandar Zeljic; Guy Katz; Clark W. Barrett (Zenodo, 2022)

Artifact for Paper Scalable Verification of GNN-Based Job Schedulers. - Haoze Wu 001; Clark W. Barrett; Mahmood Sharif; Nina Narodytska; Gagandeep Singh 001 (Zenodo, 2022)

Convex Bounds on the Softmax Function with Applications to Robustness Verification. - Dennis Wei; Haoze Wu 001; Min Wu; Pin-Yu Chen; Clark W. Barrett; Eitan Farchi (AISTATS, 2023)

Lightweight Online Learning for Sets of Related Problems in Automated Reasoning. - Haoze Wu 001; Christopher Hahn; Florian Lonsing; Makai Mann; Raghuram Ramanujan; Clark W. Barrett (FMCAD, 2023)

Soy: An Efficient MILP Solver for Piecewise-Affine Systems. - Haoze Wu 001; Min Wu; Dorsa Sadigh; Clark W. Barrett (IROS, 2023)

VeriX: Towards Verified Explainability of Deep Neural Networks. - Min Wu; Haoze Wu 001; Clark W. Barrett (NeurIPS, 2023)

Toward Certified Robustness Against Real-World Distribution Shifts. - Haoze Wu 001; Teruhiro Tagomori; Alexander Robey; Fengjun Yang; Nikolai Matni; George J. Pappas; Hamed Hassani; Corina S. Pasareanu; Clark W. Barrett (SaTML, 2023)

Verifying MILP Certificates with SMT Solvers. - Runtian Zhou; Haoze Wu 001; Hammurabi Mendes; Jonad Pulaj (CoRR, 2023)

Global optimization of objective functions represented by ReLU networks. - Christopher A. Strong; Haoze Wu 001; Aleksandar Zeljic; Kyle D. Julian; Guy Katz; Clark W. Barrett; Mykel J. Kochenderfer (Mach. Learn., 2023)

Towards Efficient Verification of Quantized Neural Networks. - Pei Huang 002; Haoze Wu 001; Yuting Yang 002; Ieva Daukantas; Min Wu; Yedi Zhang; Clark W. Barrett (AAAI, 2024)

Marabou 2.0: A Versatile Formal Analyzer of Neural Networks. - Haoze Wu 001; Omri Isac; Aleksandar Zeljic; Teruhiro Tagomori; Matthew L. Daggitt; Wen Kokke; Idan Refaeli; Guy Amir; Kyle Julian; Shahaf Bassan; Pei Huang 002; Ori Lahav 002; et al. (CAV (2), 2024)

Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates. - Udayan Mandal; Guy Amir; Haoze Wu 001; Ieva Daukantas; Fletcher Lee Newell; Umberto J. Ravaioli; Baoluo Meng; Michael Durling; Milan Ganai; Tobey Shim; Guy Katz; Clark W. Barrett (FMCAD, 2024)

Lemur: Integrating Large Language Models in Automated Program Verification. - Haoze Wu 001; Clark W. Barrett; Nina Narodytska (ICLR, 2024)

Parallel Verification for δ-Equivalence of Neural Network Quantization. - Pei Huang 002; Yuting Yang 002; Haoze Wu 001; Ieva Daukantas; Min Wu; Fuqi Jia; Clark W. Barrett (SAIV, 2024)

Safe and Reliable Training of Learning-Based Aerospace Controllers. - Udayan Mandal; Guy Amir; Haoze Wu 001; Ieva Daukantas; Fletcher Lee Newell; Umberto J. Ravaioli; Baoluo Meng; Michael Durling; Kerianne Hobbs; Milan Ganai; Tobey Shim; Guy Katz; et al. (CoRR, 2024)

Better Verified Explanations with Applications to Incorrectness and Out-of-Distribution Detection. - Min Wu; Xiaofu Li; Haoze Wu 001; Clark W. Barrett (CoRR, 2024)

The Fifth International Verification of Neural Networks Competition (VNN-COMP 2024): Summary and Results. - Christopher Brix; Stanley Bak; Taylor T. Johnson; Haoze Wu 001 (CoRR, 2024)

Neural Network Verification is a Programming Language Challenge. - Lucas C. Cordeiro; Matthew L. Daggitt; Julien Girard-Satabin; Omri Isac; Taylor T. Johnson; Guy Katz; Ekaterina Komendantskaya; Augustin Lemesle; Edoardo Manino; Artjoms Sinkarovs; Haoze Wu 001 (ESOP (1), 2025)

Proof-Driven Clause Learning in Neural Network Verification. - Omri Isac; Idan Refaeli; Haoze Wu 001; Clark W. Barrett; Guy Katz (CoRR, 2025)

Cubing for Tuning. - Haoze Wu 001; Clark W. Barrett; Nina Narodytska (CoRR, 2025)

Abstraction-Based Proof Production in Formal Verification of Neural Networks. - Yizhak Yisrael Elboher; Omri Isac; Guy Katz; Tobias Ladner; Haoze Wu 001 (CoRR, 2025)