# Learning Unbiased Representations via R'enyi Minimization

arXiv: https://arxiv.org/abs/2009.03183 · HTML: https://mlsanity.com/paper/2009.03183/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2020.

## Built on by (most cited first, 18 of 18)

- [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)
- [Fairness without the sensitive attribute via Causal Variational Autoencoder](https://mlsanity.com/paper/2109.04999/) (2021)
- [Utility-Fairness Trade-Offs and How to Find Them](https://mlsanity.com/paper/2404.09454/) (2024)
- [On Characterizing the Trade-off in Invariant Representation Learning](https://mlsanity.com/paper/2109.03386/) (2021)
- [On the Inductive Biases of Demographic Parity-based Fair Learning Algorithms](https://mlsanity.com/paper/2402.18129/) (2024)
- [WLD-Reg: A Data-dependent Within-layer Diversity Regularizer](https://mlsanity.com/paper/2301.01352/) (2023)
- [Inducing Group Fairness in Prompt-Based Language Model Decisions](https://mlsanity.com/paper/2406.16738/) (2024)
- [When mitigating bias is unfair: multiplicity and arbitrariness in algorithmic group fairness](https://mlsanity.com/paper/2302.07185/) (2023)
- [Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification](https://mlsanity.com/paper/2307.05728/) (2023)
- [Learning Fair Representations with High-Confidence Guarantees](https://mlsanity.com/paper/2310.15358/) (2023)
- [Adversarial Dependence Minimization](https://mlsanity.com/paper/2502.03227/) (2025)
- [pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learning](https://mlsanity.com/paper/2503.14925/) (2025)
- [OptiGrad: A Fair and more Efficient Price Elasticity Optimization via a Gradient Based Learning](https://mlsanity.com/paper/2404.10275/) (2024)
- [A Critical Review of Predominant Bias in Neural Networks](https://mlsanity.com/paper/2502.11031/) (2025)
- [Deep Fair Learning: Task-Aware Fair Representations via Joint Distance-Covariance Regularization](https://mlsanity.com/paper/2504.06470/) (2025)
- [Fair Representation Learning with Controllable High Confidence Guarantees via Adversarial Inference](https://mlsanity.com/paper/2510.21017/) (2025)
- [Toward Unifying Group Fairness Evaluation from a Sparsity Perspective](https://mlsanity.com/paper/2511.00359/) (2025)

## Abstract (arXiv)

In recent years, significant work has been done to include fairness constraints in the training objective of machine learning algorithms. Many state-of the-art algorithms tackle this challenge by learning a fair representation which captures all the relevant information to predict the output Y while not containing any information about a sensitive attribute S. In this paper, we propose an adversarial algorithm to learn unbiased representations via the Hirschfeld-Gebelein-Renyi (HGR) maximal correlation coefficient. We leverage recent work which has been done to estimate this coefficient by learning deep neural network transformations and use it as a minmax game to penalize the intrinsic bias in a multi dimensional latent representation. Compared to other dependence measures, the HGR coefficient captures more information about the non-linear dependencies with the sensitive variable, making the algorithm more efficient in mitigating bias in the representation. We empirically evaluate and compare our approach and demonstrate significant improvements over existing works in the field.