# Fair Clustering via Hierarchical Fair-Dirichlet Process

arXiv: https://arxiv.org/abs/2305.17557 · HTML: https://mlsanity.com/paper/2305.17557/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 47 references that are in the map.
- First posted on arXiv 27 May 2023.

## Builds on (most cited first, 19 of 19)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Fair Clustering Through Fairlets](https://mlsanity.com/paper/1802.05733/) (2018)
- [Fair Algorithms for Clustering](https://mlsanity.com/paper/1901.02393/) (2019)
- [Guarantees for Spectral Clustering with Fairness Constraints](https://mlsanity.com/paper/1901.08668/) (2019)
- [Proportionally Fair Clustering](https://mlsanity.com/paper/1905.03674/) (2019)
- [Fair k-Center Clustering for Data Summarization](https://mlsanity.com/paper/1901.08628/) (2019)
- [Individual Fairness for k-Clustering](https://mlsanity.com/paper/2002.06742/) (2020)
- [Probabilistic Fair Clustering](https://mlsanity.com/paper/2006.10916/) (2020)
- [Better Algorithms for Individually Fair k-Clustering](https://mlsanity.com/paper/2106.12150/) (2021)
- [Fair Correlation Clustering](https://mlsanity.com/paper/2002.02274/) (2020)
- [Fair Hierarchical Clustering](https://mlsanity.com/paper/2006.10221/) (2020)
- [Nonparametric independence testing via mutual information](https://mlsanity.com/paper/1711.06642/) (2017)
- [A Notion of Individual Fairness for Clustering](https://mlsanity.com/paper/2006.04960/) (2020)
- [Concentration Inequalities for Multinoulli Random Variables](https://mlsanity.com/paper/2001.11595/) (2020)
- [Fair Clustering with Multiple Colors](https://mlsanity.com/paper/2002.07892/) (2020)
- [A Constant Approximation for Colorful k-Center](https://mlsanity.com/paper/1907.08906/) (2019)
- [Clustering consistency with Dirichlet process mixtures](https://mlsanity.com/paper/2205.12924/) (2022)
- [Non-Uniform Sampling of Fixed Margin Binary Matrices](https://mlsanity.com/paper/2007.15043/) (2020)
- [Clustering and Variable Selection in the Presence of Mixed Variable Types and Missing Data](https://mlsanity.com/paper/1703.08741/) (2017)

## Built on by (most cited first, 1 of 1)

- [A unified Bayesian framework for interval hypothesis testing in clinical trials](https://mlsanity.com/paper/2402.13890/) (2024)

## Abstract (arXiv)

The advent of ML-driven decision-making and policy formation has led to an increasing focus on algorithmic fairness. As clustering is one of the most commonly used unsupervised machine learning approaches, there has naturally been a proliferation of literature on em fair clustering. A popular notion of fairness in clustering mandates the clusters to be em balanced, i.e., each level of a protected attribute must be approximately equally represented in each cluster. Building upon the original framework, this literature has rapidly expanded in various aspects. In this article, we offer a novel model-based formulation of fair clustering, complementing the existing literature which is almost exclusively based on optimizing appropriate objective functions.