Given a metric space, the (k, z)-clustering problem consists of finding k centers such that the
sum of the of distances raised to the power z of every point to its closest center is minimized …

In this paper, we consider the problem of finding high dimensional power means: given a set
$ A $ of $ n $ points in $\R^ d $, find the point $ m $ that minimizes the sum of Euclidean …

Center-based clustering is a fundamental primitive for data analysis and becomes very
challenging for large datasets. In this paper, we focus on the popular $ k $-center variant …

Given a set X of n points in a metric space, the problem of diversity maximization is to extract
a set S of k points from X so that the diversity of S is maximized. This problem is essential in …

Diversity is an important principle in data selection and summarization, facility location, and
recommendation systems. Our work focuses on maximizing diversity in data selection, while …

We study core-set construction algorithms for the task of Diversity Maximization under
fairness/partition constraint. Given a set of points $ P $ in a metric space partitioned into $ m …

Diversity maximization is a fundamental problem with broad applications in data
summarization, web search, and recommender systems. Given a set X of n elements, the …

Clustering is a pivotal primitive for unsupervised learning and data analysis. A popular
variant is the (k, ℓ)-clustering problem, where, given a pointset P from a metric space, one …

Given an $ n $-point metric space $(\mathcal {X}, d) $ where each point belongs to one of $
m= O (1) $ different categories or groups and a set of integers $ k_1,\ldots, k_m $, the fair …

Diversity maximization is a fundamental problem with wide applications in data
summarization, web search, and recommender systems. Given a set X of n elements, it asks …