Bio

Hi, this is Canyu Chen (陈灿宇). I am currently a second-year Ph.D. student in the Department of Computer Science at Illinois Institute of Technology (IIT) since Fall 2021, advised by Prof. Kai Shu. Before joining IIT, I received my B.S. in Computer Science from the University of Chinese Academy of Sciences (UCAS) in 2020.

My research interests focus on Transparent, Efficient, and Trustworthy Machine Learning (Fairness/Interpretability/Privacy/Robustness) with the specific applications in Social Computing, Natural Language Processing and Healthcare.

News

[06/2023] Will attend FAccT 2023 as a volunteer. Welcome to Chicago and glad to connect!
[05/2023] One paper accepted at EACL 2023 and will attend online. Welcome to our poster!
[04/2023] Glad to be invited by Prof. Lu Cheng to give a talk on AI Fairness at UIC [Slides]
[11/2022] Attend NeurIPS 2022 in person. See you at New Orleans!
[08/2022] Attend KDD 2022 in person. Glad to meet old friends and make new friends!

Publications

2023

• PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learners.
  Canyu Chen, Kai Shu.
  Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2023, Main Conference Long Paper)
  Advances in Neural Information Processing Systems workshop on Efficient Natural Language and Speech Processing (ENLSP@NeurIPS 2022, Oral (spotlight)).
  [code] [arXiv] [youtube] [bilibili] [slides] [poster] [workshop version]

• Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approach.
  Yueqing Liang, Canyu Chen, Tian Tian, Kai Shu.
  Frontiers in Big Data 2023.
  [paper] [arXiv]

• Attacking Fake News Detectors via Manipulating News Social Engagement.
  Haoran Wang, Yingtong Dou, Canyu Chen, Lichao Sun, Philip S. Yu, Kai Shu.
  Proceedings of 32nd The ACM Web Conference (WWW 2023).
  Media Coverage : [ACM Showcase].
  [arXiv]

2022

• Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues.
  Canyu Chen*, Haoran Wang*, Matthew Shapiro, Yunyu Xiao, Fei Wang, Kai Shu. (*equal contributions)
  arXiv preprint. Nov. 2022.
  [arXiv]

• When Fairness Meets Privacy: Fair Classification with Semi-Private Sensitive Attributes.
  Canyu Chen, Yueqing Liang, Xiongxiao Xu, Shangyu Xie, Yuan Hong, Kai Shu.
  Advances in Neural Information Processing Systems workshop on Trustworthy and Socially Responsible Machine Learning (TSRML@NeurIPS 2022) and workshop on Algorithmic Fairness through the Lens of Causality and Privacy (AFCP@NeurIPS 2022).
  [arXiv] [Video] [Slides] [Poster] [TSRML version] [AFCP version]

• Artificial Intelligence Algorithms for Treatment of Diabetes.
  Mudassir M. Rashid, Mohammad Reza Askari, Canyu Chen, Yueqing Liang, Kai Shu, Ali Cinar.
  Algorithms 2022.
  [Paper]

• BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs.
  Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H. Chen, Zhihao Jia, Philip S. Yu.
  Advances in Neural Information Processing Systems (NeurIPS 2022), Datasets and Benchmarks Track.
  [code] [arXiv]

• PyGOD: A Python Library for Graph Outlier Detection.
  Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, George H. Chen, Zhihao Jia, Philip S. Yu.
  arXiv preprint. Apr. 2022.
  [code] [arXiv]

Talks

• [04/18/2022] Fairness in AI: An Introduction at UIC [Slides]