Gradient sparsification for communication-efficient distributed optimization
Modern large-scale machine learning applications require stochastic optimization
algorithms to be implemented on distributed computational architectures. A key bottleneck is …
algorithms to be implemented on distributed computational architectures. A key bottleneck is …
Distributed mean estimation with limited communication
Motivated by the need for distributed learning and optimization algorithms with low
communication cost, we study communication efficient algorithms for distributed mean …
communication cost, we study communication efficient algorithms for distributed mean …
A tighter analysis of spectral clustering, and beyond
P Macgregor, H Sun - International Conference on Machine …, 2022 - proceedings.mlr.press
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 …
some graph G=(V_G, E_G) into R^ k using k eigenvectors of some matrix of G, and applies k …
Clustering billions of reads for DNA data storage
Storing data in synthetic DNA offers the possibility of improving information density and
durability by several orders of magnitude compared to current storage technologies …
durability by several orders of magnitude compared to current storage technologies …
Reputation-based coalition formation for secure self-organized and scalable sharding in IoT blockchains with mobile-edge computing
A Asheralieva, D Niyato - IEEE Internet of Things Journal, 2020 - ieeexplore.ieee.org
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 …
scheme for the Internet-of-Things (IoT) blockchains that can guarantee system security …
On weighted graph sparsification by linear sketching
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 …
sparsifier of an unweighted undirected graph by taking a near-linear number of linear …
The communication complexity of optimization
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 …
problems. We start with the problem of solving a linear system. Suppose there is a …
Distributed partial clustering
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 …
data, largely due to the fact that massive datasets are often collected and stored in different …
Distributed graph clustering and sparsification
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 …
algorithm design, machine learning, data mining, and analysis of social networks. Over the …
Distributed -Clustering for Data with Heavy Noise
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 …
(or the $(k, z) $-center/median/means problems) in the distributed setting. Most previous …