# Fairness-Aware Neural R'eyni Minimization for Continuous Features

arXiv: https://arxiv.org/abs/1911.04929 · HTML: https://mlsanity.com/paper/1911.04929/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2019.

## Built on by (most cited first, 15 of 15)

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [A Stochastic Optimization Framework for Fair Risk Minimization](https://mlsanity.com/paper/2102.12586/) (2021)
- [FairCanary: Rapid Continuous Explainable Fairness](https://mlsanity.com/paper/2106.07057/) (2021)
- [Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition](https://mlsanity.com/paper/2307.11333/) (2023)
- [Scalable Infomin Learning](https://mlsanity.com/paper/2302.10701/) (2023)
- [A Unified View of Group Fairness Tradeoffs Using Partial Information Decomposition](https://mlsanity.com/paper/2406.04562/) (2024)
- [Inducing Group Fairness in Prompt-Based Language Model Decisions](https://mlsanity.com/paper/2406.16738/) (2024)
- [Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes](https://mlsanity.com/paper/2311.05866/) (2023)
- [Implementing Fair Regression In The Real World](https://mlsanity.com/paper/2104.04353/) (2021)
- [AI Fairness: from Principles to Practice](https://mlsanity.com/paper/2207.09833/) (2022)
- [Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification](https://mlsanity.com/paper/2307.05728/) (2023)
- [Dr. FERMI: A Stochastic Distributionally Robust Fair Empirical Risk Minimization Framework](https://mlsanity.com/paper/2309.11682/) (2023)
- [Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression](https://mlsanity.com/paper/2506.11627/) (2025)
- [From Discrete to Continuous: Deep Fair Clustering With Transferable Representations](https://mlsanity.com/paper/2403.16201/) (2024)
- [Fairness-Aware Insurance Pricing: A Multi-Objective Optimization Approach](https://mlsanity.com/paper/2512.24747/) (2025)

## Abstract (arXiv)

The past few years have seen a dramatic rise of academic and societal interest in fair machine learning. While plenty of fair algorithms have been proposed recently to tackle this challenge for discrete variables, only a few ideas exist for continuous ones. The objective in this paper is to ensure some independence level between the outputs of regression models and any given continuous sensitive variables. For this purpose, we use the Hirschfeld-Gebelein-R'enyi (HGR) maximal correlation coefficient as a fairness metric. We propose two approaches to minimize the HGR coefficient. First, by reducing an upper bound of the HGR with a neural network estimation of the chi2 divergence. Second, by minimizing the HGR directly with an adversarial neural network architecture. The idea is to predict the output Y while minimizing the ability of an adversarial neural network to find the estimated transformations which are required to predict the HGR coefficient. We empirically assess and compare our approaches and demonstrate significant improvements on previously presented work in the field.