The Densest Subgraph Problem requires us to find, in a given graph, a subset of vertices
whose induced subgraph maximizes a measure of density. The problem has received a …

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 …

In a recent work,\cite {chierichetti2017fair} studied the following``fair''variants of classical
clustering problems such as k-means and k-median: given a set of n data points in R^ d and …

A clustering may be considered as fair on pre-specified sensitive attributes if the proportions
of sensitive attribute groups in each cluster reflect that in the dataset. In this paper, we …

The topology of the hyperlink graph among pages expressing different opinions may
influence the exposure of readers to diverse content. Structural bias may trap a reader in …

Finding dense subgraphs of a large graph is a standard problem in graph mining that has
been studied extensively both for its theoretical richness and its many practical applications …

We introduce a novel problem for diversity-aware clustering. We assume that the potential
cluster centers belong to a set of groups defined by protected attributes, such as ethnicity …

Streaming submodular maximization is a natural model for the task of selecting a
representative subset from a large-scale dataset. If datapoints have sensitive attributes such …

A common distinction in fair machine learning, in particular in fair classification, is between
group fairness and individual fairness. In the context of clustering, group fairness has been …

An instance of colorful k-center consists of points in a metric space that are colored red or
blue, along with an integer k and a coverage requirement for each color. The goal is to find …