We study a recently proposed large-scale distributed learning paradigm, namely Federated
Learning, where the worker machines are end users' own devices. Statistical and …

Clustering is a fundamental tool in data mining. It partitions points into groups (clusters) and
may be used to make decisions for each point based on its group. However, this process …

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 …

Parameterization and approximation are two popular ways of coping with NP-hard
problems. More recently, the two have also been combined to derive many interesting …

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 …

Abstract k-means and k-median clustering are powerful unsupervised machine learning
techniques. However, due to complicated dependencies on all the features, it is challenging …

Abstract k-Means is a well-studied clustering problem that finds applications in many fields
related to unsupervised learning. It is known that k-means clustering is highly sensitive to the …

In this paper, we consider the colorful $ k $-center problem, which is a generalization of the
well-known $ k $-center problem. Here, we are given red and blue points in a metric space …

The Uncapacitated Facility Location (UFL) problem is one of the most fundamental
clustering problems: Given a set of clients C and a set of facilities F in a metric space (C∪ F …