Clustering algorithms are a class of unsupervised machine learning (ML) algorithms that
feature ubiquitously in modern data science, and play a key role in many learning-based …

Tradition K-means clustering algorithm is easy to fall into local optimum, poor clustering
effect on large capacity data and uneven distribution of clustering centroids. To solve these …

In recent years, designing fairness-aware methods has received much attention in various
domains, including machine learning, natural language processing, and information …

Studies conducted on Western, Educated, Industrialized, Rich, and Democratic (WEIRD)
samples are considered atypical of the world's population and may not accurately represent …

Data-driven algorithms are studied and deployed in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing community of …

We consider the $ k $-clustering problem with $\ell_p $-norm cost, which includes $ k $-
median, $ k $-means and $ k $-center, under an individual notion of fairness proposed by …

We present an (e^{O (p)}(log\ell)/(log log\ell))-approximation algorithm for socially fair
clustering with the l_p-objective. In this problem, we are given a set of points in a metric …

This paper studies the fair range clustering problem in which the data points are from
different demographic groups and the goal is to pick $ k $ centers with the minimum …

We introduce the (p, q)-Fair Clustering problem. In this problem, we are given a set of points
P and a collection of different weight functions W. We would like to find a clustering which …

We propose simple active sampling and reweighting strategies for optimizing min-max
fairness that can be applied to any classification or regression model learned via loss …