research
∙
08/08/2023
Preserving Sparsity and Privacy in Straggler-Resilient Distributed Matrix Computations
Existing approaches to distributed matrix computations involve allocatin...
share
research
∙
05/08/2023
Communication complexity of entanglement assisted multi-party computation
We consider a quantum and classical version multi-party function computa...
research
∙
04/26/2023
Coded matrix computation with gradient coding
Polynomial based approaches, such as the Mat-Dot and entangled polynomia...
research
∙
02/23/2023
Coded Matrix Computations for D2D-enabled Linearized Federated Learning
Federated learning (FL) is a popular technique for training a global mod...
research
∙
01/30/2023
Distributed Matrix Computations with Low-weight Encodings
Straggler nodes are well-known bottlenecks of distributed matrix computa...
research
∙
08/17/2022
Efficient Detection and Filtering Systems for Distributed Training
A plethora of modern machine learning tasks requires the utilization of ...
research
∙
09/24/2021
A Unified Treatment of Partial Stragglers and Sparse Matrices in Coded Matrix Computation
The overall execution time of distributed matrix computations is often d...
research
∙
08/05/2021
Aspis: A Robust Detection System for Distributed Learning
State of the art machine learning models are routinely trained on large ...
research
∙
12/11/2020
Coded sparse matrix computation schemes that leverage partial stragglers
Distributed matrix computations over large clusters can suffer from the ...
research
∙
10/10/2020
ByzShield: An Efficient and Robust System for Distributed Training
Training of large scale models on distributed clusters is a critical com...
research
∙
02/28/2020
Federated Over-the-Air Subspace Learning from Incomplete Data
Federated learning refers to a distributed learning scenario in which us...
research
∙
02/10/2020
Straggler-resistant distributed matrix computation via coding theory
The current BigData era routinely requires the processing of large scale...
research
∙
10/15/2019
Numerically stable coded matrix computations via circulant and rotation matrix embeddings
Several recent works have used coding-theoretic ideas for mitigating the...
research
∙
08/14/2019
Resolvable Designs for Speeding up Distributed Computing
Distributed computing frameworks such as MapReduce are often used to pro...
research
∙
07/18/2019
Random Convolutional Coding for Robust and Straggler Resilient Distributed Matrix Computation
Distributed matrix computations (matrix-vector and matrix-matrix multipl...
research
∙
07/16/2019
Asynchronous Coded Caching
Coded caching is a technique that promises huge reductions in network tr...
research
∙
01/30/2019
Universally Decodable Matrices for Distributed Matrix-Vector Multiplication
Coded computation is an emerging research area that leverages concepts f...
research
∙
01/25/2019
Distributed Matrix-Vector Multiplication: A Convolutional Coding Approach
Distributed computing systems are well-known to suffer from the problem ...
research
∙
01/22/2019
CAMR: Coded Aggregated MapReduce
Many big data algorithms executed on MapReduce-like systems have a shuff...
research
∙
11/06/2018
Erasure coding for distributed matrix multiplication for matrices with bounded entries
Distributed matrix multiplication is widely used in several scientific d...
research
∙
09/17/2018
C^3LES: Codes for Coded Computation that Leverage Stragglers
In distributed computing systems, it is well recognized that worker node...
research
∙
05/09/2018
Zero-error Function Computation on a Directed Acyclic Network
We study the rate region of variable-length source-network codes that ar...
research
∙
02/08/2018