We provide novel coded computation strategies for distributed matrix-matrix products that
outperform the recent “Polynomial code” constructions in recovery threshold, ie, the required …

In large-scale distributed computing clusters, such as Amazon EC2, there are several types
of “system noise” that can result in major degradation of performance: system failures …

This paper has two main contributions. First, we propose a novel coding technique-
Generalized PolyDot-for matrix-vector products that advances on existing techniques for …

Large matrix multiplications are central to large-scale machine learning applications. These
operations are often carried out on a distributed computing platform with a master server and …

We provide novel coded computation strategies for distributed matrix-matrix products that
outperform the recent “Polynomial code” constructions in recovery threshold, ie, the required …

We consider the problem of coded computing, where a computational task is performed in a
distributed fashion in the presence of adversarial workers. We propose techniques to break …

Tensor operations, such as matrix multiplication, are central to large-scale machine learning
applications. These operations can be carried out on a distributed computing platform with a …

We propose a novel application of coded computing to the problem of the nearest neighbor
estimation using MatDot Codes (Fahim et al., Allerton'17) that are known to be optimal for …

Cloud providers have recently introduced new offerings whereby spare computing
resources are accessible at discounts compared to on-demand computing. Exploiting such …

We consider the critical problem of distributed learning over data while keeping it private
from the computational servers. The state-of-the-art approaches to this problem rely on …