Explaining vulnerabilities to adversarial machine learning through visual analytics Y Ma, T Xie, J Li, R Maciejewski IEEE transactions on visualization and computer graphics 26 (1), 1075-1085, 2019 | 84 | 2019 |
Fairrankvis: A visual analytics framework for exploring algorithmic fairness in graph mining models T Xie, Y Ma, J Kang, H Tong, R Maciejewski IEEE transactions on visualization and computer graphics 28 (1), 368-377, 2021 | 32 | 2021 |
Multifair: Multi-group fairness in machine learning J Kang, T Xie, X Wu, R Maciejewski, H Tong arXiv preprint arXiv:2105.11069, 2021 | 21 | 2021 |
Infofair: Information-theoretic intersectional fairness J Kang, T Xie, X Wu, R Maciejewski, H Tong 2022 IEEE International Conference on Big Data (Big Data), 1455-1464, 2022 | 20 | 2022 |
Auditing the sensitivity of graph-based ranking with visual analytics T Xie, Y Ma, H Tong, MT Thai, R Maciejewski IEEE Transactions on Visualization and Computer Graphics 27 (2), 1459-1469, 2020 | 10 | 2020 |
Explaining the Vulnerabilities of Machine Learning through Visual Analytics T Xie Arizona State University, 2023 | | 2023 |