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A Distributionally Robust Optimization Approach to DP-based Fair Learning
Authors:
(1) Haoyu LEI, Department of Computer Science and Engineering, The Chinese University of Hong Kong (hylei22@cse.cuhk.edu.hk);
(2) Amin Gohari, Department of Information Engineering, The Chinese University of Hong Kong (agohari@ie.cuhk.edu.hk);
(3) Farzan Farnia, Department of Computer Science and Engineering, The Chinese University of Hong Kong (farnia@cse.cuhk.edu.hk).
Table of Links
3 Preliminaries
3.1 Fair Supervised Learning and 3.2 Fairness Criteria
3.3 Dependence Measures for Fair Supervised Learning
4 Inductive Biases of DP-based Fair Supervised Learning
4.1 Extending the Theoretical Results to Randomized Prediction Rule
5 A Distributionally Robust Optimization Approach to DP-based Fair Learning
6 Numerical Results
6.2 Inductive Biases of Models trained in DP-based Fair Learning
6.3 DP-based Fair Classification in Heterogeneous Federated Learning
Appendix B Additional Results for Image Dataset
5 A Distributionally Robust Optimization Approach to DP-based Fair Learning
This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.
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