Haoze Wu

According to our database, Haoze Wu authored at least 47 papers between 2017 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Viverra: Text-to-Code with Guarantees.

Haoze Wu, Rocky Klopfenstein, Keith Farkas, Nina Narodytska

CoRR, May, 2026

Not All Invariants Are Equal: Curating Training Data to Accelerate Program Verification with SLMs.

Ido Pinto, Yizhak Yisrael Elboher, Haoze Wu, Nina Narodytska, Guy Katz

CoRR, March, 2026

Incremental Neural Network Verification via Learned Conflicts.

Raya Elsaleh, Liam Davis, Haoze Wu, Guy Katz

CoRR, March, 2026

SpotIt+: Verification-based Text-to-SQL Evaluation with Database Constraints.

Andrew Tremante, Yang He, Rocky Klopfenstein, Yuepeng Wang, Nina Narodytska, Haoze Wu

CoRR, March, 2026

Formal Synthesis of Certifiably Robust Neural Lyapunov-Barrier Certificates.

Chengxiao Wang, Haoze Wu, Gagandeep Singh

CoRR, February, 2026

Proof Minimization in Neural Network Verification.

Omri Isac, Idan Refaeli, Haoze Wu, Clark W. Barrett, Guy Katz

Proceedings of the Verification, Model Checking, and Abstract Interpretation, 2026

Efficiently Computing Compact Formal Explanations.

Min Wu, Xiaofu Li, Haoze Wu, Clark W. Barrett

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

Cubing for Tuning.

Haoze Wu, Clark W. Barrett, Nina Narodytska

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

Parameterized Abstract Interpretation for Transformer Verification.

Pei Huang, Dennis Wei, Omri Isac, Haoze Wu, Min Wu, Clark W. Barrett

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025

The 6th International Verification of Neural Networks Competition (VNN-COMP 2025): Summary and Results.

Konstantin Kaulen, Tobias Ladner, Stanley Bak, Christopher Brix, Hai Duong, Thomas Flinkow, Taylor T. Johnson, Lukas Koller, Edoardo Manino, ThanhVu H. Nguyen, Haoze Wu

CoRR, December, 2025

SpotIt: Evaluating Text-to-SQL Evaluation with Formal Verification.

Rocky Klopfenstein, Yang He, Andrew Tremante, Yuepeng Wang, Nina Narodytska, Haoze Wu

CoRR, October, 2025

Abstraction-Based Proof Production in Formal Verification of Neural Networks.

Yizhak Yisrael Elboher, Omri Isac, Guy Katz, Tobias Ladner, Haoze Wu

CoRR, June, 2025

Proof-Driven Clause Learning in Neural Network Verification.

Omri Isac, Idan Refaeli, Haoze Wu, Clark W. Barrett, Guy Katz

CoRR, March, 2025

Abstraction-Based Proof Production in Formal Verification of Neural Networks (Extended Abstract).

Yizhak Yisrael Elboher, Omri Isac, Guy Katz, Tobias Ladner, Haoze Wu

Proceedings of the AI Verification - Second International Symposium, 2025

Per-Instance Subproblem Generation for Strategy Selection in SMT.

Amalee Wilson, Nina Narodytska, Clark W. Barrett, Haoze Wu

Proceedings of the 25th Conference on Formal Methods in Computer-Aided Design, 2025

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

Proceedings of the Programming Languages and Systems, 2025

2024

The Fifth International Verification of Neural Networks Competition (VNN-COMP 2024): Summary and Results.

Christopher Brix, Stanley Bak, Taylor T. Johnson, Haoze Wu

CoRR, 2024

Better Verified Explanations with Applications to Incorrectness and Out-of-Distribution Detection.

Min Wu, Xiaofu Li, Haoze Wu, Clark W. Barrett

CoRR, 2024

Safe and Reliable Training of Learning-Based Aerospace Controllers.

Udayan Mandal, Guy Amir, Haoze Wu, Ieva Daukantas, Fletcher Lee Newell, Umberto J. Ravaioli, Baoluo Meng, Michael Durling, Kerianne Hobbs, Milan Ganai, Tobey Shim, Guy Katz, Clark W. Barrett

CoRR, 2024

Parallel Verification for δ-Equivalence of Neural Network Quantization.

Pei Huang, Yuting Yang, Haoze Wu, Ieva Daukantas, Min Wu, Fuqi Jia, Clark W. Barrett

Proceedings of the AI Verification - First International Symposium, 2024

Lemur: Integrating Large Language Models in Automated Program Verification.

Haoze Wu, Clark W. Barrett, Nina Narodytska

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates.

Udayan Mandal, Guy Amir, Haoze Wu, Ieva Daukantas, Fletcher Lee Newell, Umberto J. Ravaioli, Baoluo Meng, Michael Durling, Milan Ganai, Tobey Shim, Guy Katz, Clark W. Barrett

Proceedings of the Formal Methods in Computer-Aided Design, 2024

Marabou 2.0: A Versatile Formal Analyzer of Neural Networks.

Haoze Wu, Omri Isac, Aleksandar Zeljic, Teruhiro Tagomori, Matthew L. Daggitt, Wen Kokke, Idan Refaeli, Guy Amir, Kyle Julian, Shahaf Bassan, Pei Huang, Ori Lahav, Min Wu, Min Zhang, Ekaterina Komendantskaya, Guy Katz, Clark W. Barrett

Proceedings of the Computer Aided Verification - 36th International Conference, 2024

Towards Efficient Verification of Quantized Neural Networks.

Pei Huang, Haoze Wu, Yuting Yang, Ieva Daukantas, Min Wu, Yedi Zhang, Clark W. Barrett

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Global optimization of objective functions represented by ReLU networks.

Christopher A. Strong, Haoze Wu, Aleksandar Zeljic, Kyle D. Julian, Guy Katz, Clark W. Barrett, Mykel J. Kochenderfer

Mach. Learn., October, 2023

Verifying MILP Certificates with SMT Solvers.

Runtian Zhou, Haoze Wu, Hammurabi Mendes, Jonad Pulaj

CoRR, 2023

Toward Certified Robustness Against Real-World Distribution Shifts.

Haoze Wu, Teruhiro Tagomori, Alexander Robey, Fengjun Yang, Nikolai Matni, George J. Pappas, Hamed Hassani, Corina S. Pasareanu, Clark W. Barrett

Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning, 2023

VeriX: Towards Verified Explainability of Deep Neural Networks.

Min Wu, Haoze Wu, Clark W. Barrett

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Soy: An Efficient MILP Solver for Piecewise-Affine Systems.

Haoze Wu, Min Wu, Dorsa Sadigh, Clark W. Barrett

IROS, 2023

Lightweight Online Learning for Sets of Related Problems in Automated Reasoning.

Haoze Wu, Christopher Hahn, Florian Lonsing, Makai Mann, Raghuram Ramanujan, Clark W. Barrett

Proceedings of the Formal Methods in Computer-Aided Design, 2023

Convex Bounds on the Softmax Function with Applications to Robustness Verification.

Dennis Wei, Haoze Wu, Min Wu, Pin-Yu Chen, Clark W. Barrett, Eitan Farchi

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Artifact for Paper Scalable Verification of GNN-Based Job Schedulers.

Haoze Wu, Clark W. Barrett, Mahmood Sharif, Nina Narodytska, Gagandeep Singh

Dataset, September, 2022

Artifact for Paper Efficient Neural Network Analysis with Sum-of-Infeasibilities.

Haoze Wu, Aleksandar Zeljic, Guy Katz, Clark W. Barrett

Dataset, February, 2022

Scalable verification of GNN-based job schedulers.

Haoze Wu, Clark W. Barrett, Mahmood Sharif, Nina Narodytska, Gagandeep Singh

Proc. ACM Program. Lang., 2022

Proof-Stitch: Proof Combination for Divide and Conquer SAT Solvers.

Abhishek Nair, Saranyu Chattopadhyay, Haoze Wu, Alex Ozdemir, Clark W. Barrett

CoRR, 2022

Efficient Neural Network Analysis with Sum-of-Infeasibilities.

Haoze Wu, Aleksandar Zeljic, Guy Katz, Clark W. Barrett

Proceedings of the Tools and Algorithms for the Construction and Analysis of Systems, 2022

On Optimizing Back-Substitution Methods for Neural Network Verification.

Tom Zelazny, Haoze Wu, Clark W. Barrett, Guy Katz

Proceedings of the 22nd Formal Methods in Computer-Aided Design, 2022

Proof-Stitch: Proof Combination for Divide-and-Conquer SAT Solvers.

Abhishek Anil Nair, Saranyu Chattopadhyay, Haoze Wu, Alex Ozdemir, Clark W. Barrett

Proceedings of the 22nd Formal Methods in Computer-Aided Design, 2022

2021

An SMT-Based Approach for Verifying Binarized Neural Networks.

Guy Amir, Haoze Wu, Clark W. Barrett, Guy Katz

Proceedings of the Tools and Algorithms for the Construction and Analysis of Systems, 2021

DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers.

Colin Paterson, Haoze Wu, John Grese, Radu Calinescu, Corina S. Pasareanu, Clark W. Barrett

Proceedings of the Computer Safety, Reliability, and Security, 2021

SAT Solving in the Serverless Cloud.

Alex Ozdemir, Haoze Wu, Clark W. Barrett

Proceedings of the Formal Methods in Computer Aided Design, 2021

2020

Parallelization Techniques for Verifying Neural Networks.

Haoze Wu, Alex Ozdemir, Aleksandar Zeljic, Kyle Julian, Ahmed Irfan, Divya Gopinath, Sadjad Fouladi, Guy Katz, Corina S. Pasareanu, Clark W. Barrett

Proceedings of the 2020 Formal Methods in Computer Aided Design, 2020

2019

Learning to Generate Industrial SAT Instances.

Haoze Wu, Raghuram Ramanujan

Proceedings of the Twelfth International Symposium on Combinatorial Search, 2019

G2SAT: Learning to Generate SAT Formulas.

Jiaxuan You, Haoze Wu, Clark W. Barrett, Raghuram Ramanujan, Jure Leskovec

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

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, Shantanu Thakoor, Haoze Wu, Aleksandar Zeljic, David L. Dill, Mykel J. Kochenderfer, Clark W. Barrett

Proceedings of the Computer Aided Verification - 31st International Conference, 2019

2017

Improve SAT-solving with Machine Learning.

Haoze Wu

CoRR, 2017

Improving SAT-solving with Machine Learning.

Haoze Wu

Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education, 2017