Publications

The asterisk * denotes alphabetical order.

Google Scholar

2026

  1. AAAI
    Efficiently Computing Compact Formal Explanations
    Min Wu, Xiaofu Li, Haoze Wu, and Clark Barrett
    In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2026
    Oral (4.6%) Presentation
    DOI Video Poster Slides
  2. AAAI
    Parameterized Abstract Interpretation for Transformer Verification
    Pei Huang, Dennis Wei, Omri Isac, Haoze Wu, Min Wu, and Clark Barrett
    In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2026
    DOI

2024

  1. Preprint
    Better Verified Explanations with Applications to Incorrectness and Out-of-Distribution Detection
    Min Wu, Xiaofu Li, Haoze Wu, and Clark Barrett
    In arXiv:2409.03060v1, 2024
    PDF arXiv
  2. CAV
    Marabou 2.0: A Versatile Formal Analyzer of Neural Networks
    Haoze Wu, Omri Isac, Aleksandar Zeljić, Teruhiro Tagomori, Matthew Daggitt, Wen Kokke, Idan Refaeli, Guy Amir, Kyle Julian, Shahaf Bassan, Pei Huang, Ori Lahav, Min Wu, Min Zhang, Ekaterina Komendantskaya, Guy Katz, and Clark Barrett
    In Proceedings of the 36th International Conference on Computer Aided Verification (CAV), 2024
    DOI PDF
  3. AAAI
    Towards Efficient Verification of Quantized Neural Networks
    Pei Huang, Haoze Wu, Yuting Yang, Ieva Daukantas, Min Wu, Yedi Zhang, and Clark Barrett
    In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2024
    Oral Presentation in the Safe, Robust and Responsible AI Track
    DOI PDF Video
  4. SAIV
    Parallel Verification for δ-Equivalence of Neural Network Quantization
    Pei Huang, Yuting Yang, Haoze Wu, Ieva Daukantas, Min Wu, Fuqi Jia, and Clark Barrett
    In International Symposium on AI Verification (SAIV), 2024
    DOI PDF

2023

  1. NeurIPS
    VeriX: Towards Verified Explainability of Deep Neural Networks
    Min Wu, Haoze Wu, and Clark Barrett
    In Proceedings of the 37th International Conference on Neural Information Processing Systems (NeurIPS), 2023
    Keynote at Stanford Center for AI Safety Annual Meeting
    PDF HTML Video Poster
  2. AISTATS
    Convex Bounds on the Softmax Function with Applications to Robustness Verification
    Dennis Wei, Haoze Wu, Min Wu, Pin-Yu Chen, Clark Barrett, and Eitan Farchi
    In International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
    PDF Video
  3. IROS
    Soy: An Efficient MILP Solver for Piecewise-Affine Systems
    Haoze Wu, Min Wu, Dorsa Sadigh, and Clark Barrett
    In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023
    DOI PDF
  4. RDDPS@ICAPS
    Policy-Specific Abstraction Predicate Selection in Neural Policy Safety Verification
    Marcel Vinzent, Min Wu, Haoze Wu, and Jörg Hoffmann
    In Proceedings of the 2nd Workshop on Reliable Data-Driven Planning and Scheduling, co-located with the International Conference on Automated Planning and Scheduling (RDDPS@ICAPS), 2023
    PDF

2022

  1. Optica
    Full Poincaré Polarimetry Enabled through Physical Inference
    Chao He, Jianyu Lin, Jintao Chang, Jacopo Antonello, Ben Dai, Jingyu Wang, Jiahe Cui, Ji Qi, Min Wu, Daniel S Elson, Peng Xi, Andrew Forbes, and Martin J Booth
    Optica, 2022
    DOI PDF

2020

  1. CVPR
    Robustness Guarantees for Deep Neural Networks on Videos
    Min Wu, and Marta Kwiatkowska
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020
    Oral (5.7%) Presentation
    DOI PDF Video
  2. EMNLP
    Assessing Robustness of Text Classification through Maximal Safe Radius Computation
    Emanuele La Malfa, Min Wu, Luca Laurenti, Benjie Wang, Anthony Hartshorn, and Marta Kwiatkowska
    In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP): Findings, 2020
    DOI PDF
  3. Theor. Comput. Sci.
    A Game-based Approximate Verification of Deep Neural Networks with Provable Guarantees
    Min Wu, Matthew Wicker, Wenjie Ruan, Xiaowei Huang, and Marta Kwiatkowska
    Theoretical Computer Science, 2020
    Invited Paper
    DOI PDF
  4. Comput. Sci. Rev.
    A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability
    Xiaowei Huang, Daniel Kroening, Wenjie Ruan, James Sharp, Youcheng Sun, Emese Thamo, Min Wu*, and Xinping Yi
    Computer Science Review, 2020
    DOI PDF
    840 Google Scholar citations
  5. PhD Thesis
    Robustness Evaluation of Deep Neural Networks with Provable Guarantees
    Min Wu
    University of Oxford, 2020
    PDF

2019

  1. IROS
    Gaze-based Intention Anticipation over Driving Manoeuvres in Semi-Autonomous Vehicles
    Min Wu, Tyron Louw, Morteza Lahijanian, Wenjie Ruan, Xiaowei Huang, Natasha Merat, and Marta Kwiatkowska
    In Proceedings of the 32nd IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019
    Oral Presentation
    DOI PDF
  2. IJCAI
    Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the Hamming Distance
    Wenjie Ruan, Min Wu, Youcheng Sun, Xiaowei Huang, Daniel Kroening, and Marta Kwiatkowska
    In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019
    Oral Presentation
    DOI PDF

2018

  1. ASE
    Concolic Testing for Deep Neural Networks
    Youcheng Sun, Min Wu, Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska, and Daniel Kroening
    In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (ASE), 2018
    DOI PDF

2017

  1. CAV
    Safety Verification of Deep Neural Networks
    Xiaowei Huang, Marta Kwiatkowska, Sen Wang, and Min Wu*
    In Proceedings of the 29th International Conference on Computer Aided Verification (CAV), 2017
    Keynote Paper at CAV 2017
    DOI PDF Video
    1.4K Google Scholar citations