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 provide simple and fast polynomial-time approximation schemes (PTASs) for several
variants of the max-sum diversification problem which, in its most basic form, is as follows …

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 …

Streaming computation plays an important role in large-scale data analysis. The sliding
window model is a model of streaming computation which also captures the recency of the …

We study a generalization of classical combinatorial graph spanners to the spectral setting.
Given a set of vectors V⊆ ℝ d, we say a set U⊆ V is an α-spectral k spanner, for k≤ d, if for …

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 …

Abstract “Composable core-sets” are an efficient framework for solving optimization
problems in massive data models. In this work, we consider efficient construction of …

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 …