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Robust and Trustworthy Machine Learning

This is a collection of our recent publications analyzing the attackability of machine learning models, and building robust learning models against noise and attacks.

  • Hongyan Bao,  Yufei Han,  Yujun Zhou,  Xin Gao,  Xiangliang Zhang. Towards Efficient and Domain-Agnostic Evasion Attack with High-dimensional Categorical Inputs. Accepted by the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023). Feb 7-14, 2023 Washington DC. (Acceptance rate = 19.6% （1,721 of 8,777 submissions))
  • Xiaoting Lyu, Yufei Han, Wei Wang, Jingkai Liu, Bin Wang, Jiqiang Liu, Xiangliang Zhang. Poisoning with Cerberus: Stealthy and Colluded Backdoor Attack against Federated Learning. Accepted by the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023). Feb 7-14, 2023 Washington DC. (Acceptance rate = 19.6% （1,721 of 8,777 submissions))
  • Hongyan Bao, Yufei Han, Yujun Zhou, Yun Shen, Xiangliang Zhang. Towards Understanding the Robustness Against Evasion Attack on Categorical Data. Accepted by ICLR 2022.
  • Helene Orsini, Hongyan Bao, Yujun Zhou, Xiangrui Xu, Yufei Han, Longyang Yi, Wei Wang, Xin Gao, and Xiangliang Zhang. AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs. The 2022 IEEE International Conference on Big Data (Big Data 2022). Regular paper. December 17-20, 2022, Osaka, Japan
  • Zhuo Yang, Yufei Han and Xiangliang Zhang. Attack Transferability Characterization for Adversarially Robust Multi-label Classification. Accepted by ECML/PKDD 2021. Virtual Conference. Aug 13-17, 2021. (Acceptance Rate = 147/685 = 21%)
  • Shijie Zhang, Hongzhi Yin, Tong Chen, Zi Huang, Lizhen Cui and Xiangliang Zhang. Graph Embedding for Recommendation against Attribute Inference Attacks. The Web Conference 2021 (WWW’21), April, 2021.  (acceptance rate of 20.6%, 357/1736).