Renjue Li (李仞珏)'s Homepage

Contact

  • Office: No. 601, Room 339, Building 5
  • Address: South Fourth Street 4#, Zhong Guan Cun, Beijing
  • email: lirj19###ios**ac*cn

About me

I am currently a Ph.D student in the group. My main research interests are:

  • Adversarial Attack and Defence on Neural Networks
  • Neural Network Verification

Education

Publications

  • Renjue Li, Tianhang Qin, Cas Widdershoven: ISS-Scenario: Scenario-based Testing in CARLA. In 18th International Symposium on Theoretical Aspects of Software Engineering (TASE 2024), 2024. BIB
  • Renjue Li, Pengfei Yang, Cheng-Chao Huang, Youcheng Sun, Bai Xue (), Lijun Zhang: 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
  • Bai Xue (), Renjue Li, Naijun Zhan (), Martin Fränzle: Reach-avoid Analysis for Stochastic Discrete-time Systems. In 2021 American Control Conference, ACC 2021, New Orleans, LA, USA, May 25-28, 2021, pages 4879-4885, 2021. DOI BIB
  • Pengfei Yang, Jianlin Li (), Jiangchao Liu, Cheng-Chao Huang, Renjue Li, Liqian Chen, Xiaowei Huang (), Lijun Zhang: Enhancing Robustness Verification for Deep Neural Networks via Symbolic Propagation. In Formal Aspects Comput. 33(3):407-435, 2021. DOI BIB
  • Pengfei Yang, Renjue Li, Jianlin Li (), Cheng-Chao Huang, Jingyi Wang, Jun Sun (), Bai Xue (), Lijun Zhang: 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
  • Renjue Li, Jianlin Li (), Cheng-Chao Huang, Pengfei Yang, Xiaowei Huang (), Lijun Zhang, Bai Xue (), Holger Hermanns (): 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