# The price for fairness in a regression framework

@article{LeGouic2020ThePF, title={The price for fairness in a regression framework}, author={Thibaut Le Gouic and Jean-Michel Loubes}, journal={ArXiv}, year={2020}, volume={abs/2005.11720} }

We consider the problem of achieving fairness in a regression framework. Fairness is here expressed as demographic parity. We provide a control over the loss of the generalization error when fairness constraint is imposed, hence computing the cost for fairness for a regressor. Then, using optimal transport theory, we provide a way to construct a fair regressor which is optimal since it achieves the optimal generalization bound. This regressor is obtained by a post-processing methodology.

## 3 Citations

### Fair Regression under Sample Selection Bias

- Computer Science2022 IEEE International Conference on Big Data (Big Data)
- 2022

This paper develops a framework for fair regression under sample selection bias when dependent variable values of a set of samples from the training data are missing as a result of another hidden process and uses the classic Heckman model for bias correction and the Lagrange duality to achieve fairness in regression based on a variety of fairness notions.

### Fairness with Continuous Optimal Transport

- Computer ScienceArXiv
- 2021

A stochastic-gradient fairness method based on a dual formulation of continuous OT that gives superior performance to discrete OT methods when little data is available to solve the OT problem, and similar performance otherwise, and is able to continually adjust the model parameters to adapt to changes in level of unfairness.

### Gradient descent algorithms for Bures-Wasserstein barycenters

- Computer Science, MathematicsCOLT
- 2020

A framework to derive global rates of convergence for both gradient descent and stochastic gradient descent despite the fact that the barycenter functional is not geodesically convex is developed by employing a Polyak-Lojasiewicz (PL) inequality.

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