Modern large-scale machine learning applications require stochastic optimization
algorithms to be implemented on distributed computational architectures. A key bottleneck is …

Motivated by the need for distributed learning and optimization algorithms with low
communication cost, we study communication efficient algorithms for distributed mean …

This work studies the classical spectral clustering algorithm which embeds the vertices of
some graph G=(V_G, E_G) into R^ k using k eigenvectors of some matrix of G, and applies k …

Storing data in synthetic DNA offers the possibility of improving information density and
durability by several orders of magnitude compared to current storage technologies …

We propose a fully distributed system architecture and a scalable self-organized sharding
scheme for the Internet-of-Things (IoT) blockchains that can guarantee system security …

A seminal work of Ahn-Guha-McGregor, PODS'12 showed that one can compute a cut
sparsifier of an unweighted undirected graph by taking a near-linear number of linear …

We consider the communication complexity of a number of distributed optimization
problems. We start with the problem of solving a linear system. Suppose there is a …

Recent years have witnessed an increasing popularity of algorithm design for distributed
data, largely due to the fact that massive datasets are often collected and stored in different …

Graph clustering is a fundamental computational problem with a number of applications in
algorithm design, machine learning, data mining, and analysis of social networks. Over the …

In this paper, we consider the $ k $-center/median/means clustering with outliers problems
(or the $(k, z) $-center/median/means problems) in the distributed setting. Most previous …