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ORCIDHaoze Wu 0001
Person information
- affiliation: Stanford University, Department of Computer Science, CA, USA
- affiliation: Davidson College, Department of Mathematics and Computer Science, NC, USA
Other persons with the same name
- Haoze Wu — disambiguation page
- Haoze Wu 0002 — Peking University, Beijing, China
- Haoze Wu 0003 — University of Science and Technology of China, National Engineering Laboratory for Brain-inspired Intelligence Technology and Application, Hefei, China
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2020 – today
- 2024
[c16]Pei Huang, Haoze Wu, Yuting Yang, Ieva Daukantas, Min Wu, Yedi Zhang, Clark W. Barrett:
Towards Efficient Verification of Quantized Neural Networks. AAAI 2024: 21152-21160- [i19]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:
Marabou 2.0: A Versatile Formal Analyzer of Neural Networks. CoRR abs/2401.14461 (2024) - 2023
- [j2]Christopher A. Strong
, Haoze Wu, Aleksandar Zeljic, Kyle D. Julian, Guy Katz, Clark W. Barrett, Mykel J. Kochenderfer:
Global optimization of objective functions represented by ReLU networks. Mach. Learn. 112(10): 3685-3712 (2023) - [c15]Dennis Wei, Haoze Wu, Min Wu, Pin-Yu Chen, Clark W. Barrett, Eitan Farchi:
Convex Bounds on the Softmax Function with Applications to Robustness Verification. AISTATS 2023: 6853-6878 - [c14]Haoze Wu, Christopher Hahn, Florian Lonsing, Makai Mann, Raghuram Ramanujan, Clark W. Barrett:
Lightweight Online Learning for Sets of Related Problems in Automated Reasoning. FMCAD 2023: 1-11 - [c13]Haoze Wu, Min Wu, Dorsa Sadigh, Clark W. Barrett:
Soy: An Efficient MILP Solver for Piecewise-Affine Systems. IROS 2023: 6281-6288 - [c12]Min Wu, Haoze Wu, Clark W. Barrett:
VeriX: Towards Verified Explainability of Deep Neural Networks. NeurIPS 2023 - [i18]Dennis Wei, Haoze Wu, Min Wu, Pin-Yu Chen, Clark W. Barrett, Eitan Farchi:
Convex Bounds on the Softmax Function with Applications to Robustness Verification. CoRR abs/2303.01713 (2023) - [i17]Haoze Wu, Min Wu, Dorsa Sadigh, Clark W. Barrett:
Soy: An Efficient MILP Solver for Piecewise-Affine Systems. CoRR abs/2303.13697 (2023) - [i16]Haoze Wu, Christopher Hahn, Florian Lonsing, Makai Mann, Raghuram Ramanujan, Clark W. Barrett:
Lightweight Online Learning for Sets of Related Problems in Automated Reasoning. CoRR abs/2305.11087 (2023) - [i15]Haoze Wu, Clark W. Barrett, Nina Narodytska:
Lemur: Integrating Large Language Models in Automated Program Verification. CoRR abs/2310.04870 (2023) - [i14]Runtian Zhou, Haoze Wu, Hammurabi Mendes, Jonad Pulaj:
Verifying MILP Certificates with SMT Solvers. CoRR abs/2312.10420 (2023) - [i13]Pei Huang, Haoze Wu, Yuting Yang, Ieva Daukantas, Min Wu, Yedi Zhang, Clark W. Barrett:
Towards Efficient Verification of Quantized Neural Networks. CoRR abs/2312.12679 (2023) - 2022
- [j1]Haoze Wu, Clark W. Barrett, Mahmood Sharif, Nina Narodytska, Gagandeep Singh:
Scalable verification of GNN-based job schedulers. Proc. ACM Program. Lang. 6(OOPSLA2): 1036-1065 (2022) - [c11]Tom Zelazny, Haoze Wu, Clark W. Barrett, Guy Katz:
On Optimizing Back-Substitution Methods for Neural Network Verification. FMCAD 2022: 17-26 - [c10]Abhishek Anil Nair, Saranyu Chattopadhyay, Haoze Wu, Alex Ozdemir, Clark W. Barrett:
Proof-Stitch: Proof Combination for Divide-and-Conquer SAT Solvers. FMCAD 2022: 84-88 - [c9]Haoze Wu, Aleksandar Zeljic, Guy Katz, Clark W. Barrett:
Efficient Neural Network Analysis with Sum-of-Infeasibilities. TACAS (1) 2022: 143-163 - [i12]Haoze Wu, Clark W. Barrett, Mahmood Sharif, Nina Narodytska, Gagandeep Singh:
Scalable Verification of GNN-based Job Schedulers. CoRR abs/2203.03153 (2022) - [i11]Haoze Wu, Aleksandar Zeljic, Guy Katz, Clark W. Barrett:
Efficient Neural Network Analysis with Sum-of-Infeasibilities. CoRR abs/2203.11201 (2022) - [i10]Haoze Wu, Teruhiro Tagomori, Alexander Robey, Fengjun Yang, Nikolai Matni, George J. Pappas, Hamed Hassani, Corina S. Pasareanu, Clark W. Barrett:
Toward Certified Robustness Against Real-World Distribution Shifts. CoRR abs/2206.03669 (2022) - [i9]Tom Zelazny, Haoze Wu, Clark W. Barrett, Guy Katz:
On Optimizing Back-Substitution Methods for Neural Network Verification. CoRR abs/2208.07669 (2022) - [i8]Abhishek Nair, Saranyu Chattopadhyay, Haoze Wu, Alex Ozdemir, Clark W. Barrett:
Proof-Stitch: Proof Combination for Divide and Conquer SAT Solvers. CoRR abs/2209.05201 (2022) - [i7]Min Wu, Haoze Wu, Clark W. Barrett:
VeriX: Towards Verified Explainability of Deep Neural Networks. CoRR abs/2212.01051 (2022) - 2021
- [c8]Alex Ozdemir, Haoze Wu, Clark W. Barrett:
SAT Solving in the Serverless Cloud. FMCAD 2021: 241-245 - [c7]Colin Paterson, Haoze Wu, John Grese, Radu Calinescu, Corina S. Pasareanu, Clark W. Barrett:
DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers. SAFECOMP 2021: 3-17 - [c6]Guy Amir, Haoze Wu, Clark W. Barrett, Guy Katz:
An SMT-Based Approach for Verifying Binarized Neural Networks. TACAS (2) 2021: 203-222 - [i6]Colin Paterson, Haoze Wu, John Grese, Radu Calinescu, Corina S. Pasareanu, Clark W. Barrett:
DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers. CoRR abs/2103.01629 (2021) - 2020
- [c5]Haoze Wu, Alex Ozdemir, Aleksandar Zeljic, Kyle Julian, Ahmed Irfan, Divya Gopinath, Sadjad Fouladi, Guy Katz, Corina S. Pasareanu, Clark W. Barrett:
Parallelization Techniques for Verifying Neural Networks. FMCAD 2020: 128-137 - [i5]Haoze Wu, Alex Ozdemir, Aleksandar Zeljic, Ahmed Irfan, Kyle Julian, Divya Gopinath, Sadjad Fouladi, Guy Katz, Corina S. Pasareanu, Clark W. Barrett:
Parallelization Techniques for Verifying Neural Networks. CoRR abs/2004.08440 (2020) - [i4]Christopher A. Strong, Haoze Wu, Aleksandar Zeljic, Kyle D. Julian, Guy Katz, Clark W. Barrett, Mykel J. Kochenderfer:
Global Optimization of Objective Functions Represented by ReLU Networks. CoRR abs/2010.03258 (2020) - [i3]Guy Amir, Haoze Wu, Clark W. Barrett, Guy Katz:
An SMT-Based Approach for Verifying Binarized Neural Networks. CoRR abs/2011.02948 (2020)
2010 – 2019
- 2019
- [c4]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:
The Marabou Framework for Verification and Analysis of Deep Neural Networks. CAV (1) 2019: 443-452 - [c3]Jiaxuan You, Haoze Wu, Clark W. Barrett, Raghuram Ramanujan, Jure Leskovec:
G2SAT: Learning to Generate SAT Formulas. NeurIPS 2019: 10552-10563 - [c2]Haoze Wu, Raghuram Ramanujan:
Learning to Generate Industrial SAT Instances. SOCS 2019: 206-207 - [i2]Jiaxuan You, Haoze Wu, Clark W. Barrett, Raghuram Ramanujan, Jure Leskovec:
G2SAT: Learning to Generate SAT Formulas. CoRR abs/1910.13445 (2019) - 2017
- [c1]Haoze Wu:
Improving SAT-solving with Machine Learning. SIGCSE 2017: 787-788 - [i1]Haoze Wu:
Improve SAT-solving with Machine Learning. CoRR abs/1710.11204 (2017)
Coauthor Index
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