Interpretable data-based explanations for fairness debugging R Pradhan, J Zhu, B Glavic, B Salimi Proceedings of the 2022 International Conference on Management of Data, 247-261, 2022 | 42 | 2022 |
Building fast and compact sketches for approximately multi-set multi-membership querying R Li, P Wang, J Zhu, J Zhao, J Di, X Yang, K Ye Proceedings of the 2021 International Conference on Management of Data, 1077 …, 2021 | 12 | 2021 |
ELLG: explainable lesion learning and generation for diabetic retinopathy detection C Lin, J Zhu, C Shen, P Hu, Q Wang International Joint Conferences on Artificial Intelligence Workshop on …, 2020 | 5 | 2020 |
Sensitive region-aware black-box adversarial attacks C Lin, S Han, J Zhu, Q Li, C Shen, Y Zhang, X Guan Information Sciences 637, 118929, 2023 | 4 | 2023 |
Consistent Range Approximation for Fair Predictive Modeling J Zhu, S Galhotra, N Sabri, B Salimi arXiv preprint arXiv:2212.10839, 2023 | 4* | 2023 |
Generating Interpretable Data-Based Explanations for Fairness Debugging using Gopher J Zhu, R Pradhan, B Glavic, B Salimi Proceedings of the 2022 International Conference on Management of Data, 2433 …, 2022 | 2 | 2022 |
Overcoming Data Biases: Towards Enhanced Accuracy and Reliability in Machine Learning J Zhu, B Salimi Data Engineering, 18, 2024 | | 2024 |