15 citations · 17 across the 5 of their papers we have counts for
9 papers
Distributed Principal Component Analysis with Limited Communication
Foivos Alimisis, Peter Davies, Bart Vandereycken +1
We study efficient distributed algorithms for the fundamental problem of principal component analysis and leading eigenvector computation on the sphere, when the data are randomly…
Component Stability in Low-Space Massively Parallel Computation
Artur Czumaj, Peter Davies, Merav Parter
We study the power and limitations of component-stable algorithms in the low-space model of Massively Parallel Computation (MPC). Recently Ghaffari, Kuhn and Uitto (FOCS 2019) intr…
Communication-Efficient Distributed Optimization with Quantized Preconditioners
Foivos Alimisis, Peter Davies, Dan Alistarh
We investigate fast and communication-efficient algorithms for the classic problem of minimizing a sum of strongly convex and smooth functions that are distributed among differ…
Simple, Deterministic, Constant-Round Coloring in the Congested Clique
Artur Czumaj, Peter Davies, Merav Parter
We settle the complexity of the -coloring and -list coloring problems in the CONGESTED CLIQUE model by presenting a simple deterministic algorithm for both problems r…
New Bounds For Distributed Mean Estimation and Variance Reduction
Peter Davies, Vijaykrishna Gurunathan, Niusha Moshrefi +2
We consider the problem of distributed mean estimation (DME), in which machines are each given a local -dimensional vector , and must cooperate to esti…
Graph Sparsification for Derandomizing Massively Parallel Computation with Low Space
Artur Czumaj, Peter Davies, Merav Parter
The Massively Parallel Computation (MPC) model is an emerging model which distills core aspects of distributed and parallel computation. It has been developed as a tool to solve (t…