A differentially private post-processing algorithm for fair regression. Supports statistical parity under the attribute-aware setting.
To reproduce our results, see the notebooks law.ipynb
and communities.ipynb
.
LP solvers. Our algorithm involves solving linear programs, and they are set up in our code using the cvxpy
package. For large-scale problems, we recommend the Gurobi optimizer for speed.
@inproceedings{xian2024DifferentiallyPrivatePost,
title = {{Differentially Private Post-Processing for Fair Regression}},
booktitle = {{Proceedings of the 41st International Conference on Machine Learning}},
author = {Xian, Ruicheng and Li, Qiaobo and Kamath, Gautam and Zhao, Han},
year = {2024}
}