Contact
- Office: No. 608, Room 339, Building 5
- Address: South Fourth Street 4#, Zhong Guan Cun, Beijing
- email:
yangpf###ios**ac*cn
About me
I am currently a Postdoctoral Researcher in the group. My main research interests are:
- Probabilistic systems
- Robustness verification of Neural Networks
Education
- 9/2015 – 7/2021: Ph.D. Student in Computer Software and Theory at Institute of Software, Chinese Academy of Sciences and University of Chinese Academy of Sciences
- 9/2011 – 6/2015: Mathematics and Applied Mathematics (Bachelor’s degree) at Peking University
Publications
- : DeepCDCL: A CDCL-based Neural Network Verification Framework. In Theoretical Aspects of Software Engineering - 18th International Symposium, TASE 2024, Guiyang, China, July 29 - August 1, 2024, Proceedings, volume 14777 of Lecture Notes in Computer Science, pages 343-355, 2024. DOI BIB
- : 前馈神经网络和循环神经网络的鲁棒性验证综述. In 软件学报 34(7):3134, 2023. DOI BIB
- : TrajPAC: Towards Robustness Verification of Pedestrian Trajectory Prediction Models. In IEEE/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, October 1-6, 2023, pages 8293-8305, 2023. DOI BIB
- : Weight Expansion: A New Perspective on Dropout and Generalization. In Trans. Mach. Learn. Res. 2022, 2022. URL BIB
- : Towards Practical Robustness Analysis for DNNs based on PAC-Model Learning. In 44th IEEE/ACM 44th International Conference on Software Engineering, ICSE 2022, Pittsburgh, PA, USA, May 25-27, 2022, pages 2189-2201, 2022. DOI BIB
- : Enhancing Robustness Verification for Deep Neural Networks via Symbolic Propagation. In Formal Aspects Comput. 33(3):407-435, 2021. DOI BIB
- : Improving Neural Network Verification through Spurious Region Guided Refinement. In Tools and Algorithms for the Construction and Analysis of Systems - 27th International Conference, TACAS 2021, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021, Luxembourg City, Luxembourg, March 27 - April 1, 2021, Proceedings, Part I, volume 12651 of Lecture Notes in Computer Science, pages 389-408, 2021. DOI BIB
- : A Near-Linear-Time Algorithm for Weak Bisimilarity on Markov Chains. In 31st International Conference on Concurrency Theory, CONCUR 2020, September 1-4, 2020, Vienna, Austria (Virtual Conference), volume 171 of LIPIcs, pages 8:1-8:20, 2020. DOI BIB
- : PRODeep: a platform for robustness verification of deep neural networks. In ESEC/FSE '20: 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Virtual Event, USA, November 8-13, 2020, pages 1630-1634, 2020. DOI BIB
- : An Initial Study on the Relationship Between Meta Features of Dataset and the Initialization of NNRW. In International Joint Conference on Neural Networks, IJCNN 2019 Budapest, Hungary, July 14-19, 2019, pages 1-8, 2019. DOI BIB
- : Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification. In Static Analysis - 26th International Symposium, SAS 2019, Porto, Portugal, October 8-11, 2019, Proceedings, volume 11822 of Lecture Notes in Computer Science, pages 296-319, 2019. DOI BIB
- : Probabilistic bisimulation for realistic schedulers. In Acta Informatica 55(6):461-488, 2018. DOI BIB
- : Distribution-Based Bisimulation for Labelled Markov Processes. In Formal Modeling and Analysis of Timed Systems - 15th International Conference, FORMATS 2017, Berlin, Germany, September 5-7, 2017, Proceedings, volume 10419 of Lecture Notes in Computer Science, pages 170-186, 2017. DOI BIB