6 citations · 11 across the 13 of their papers we have counts for
16 papers
Approximate Distributed Coded Computing: Polynomial Codes and Randomized Sketching
Neophytos Charalambides, Arya Mazumdar
Coded computing is a distributed paradigm that uses coding theory to introduce \textit{redundancy} and overcome bottlenecks in large-scale systems. In the same vein, randomized num…
Distributed Hybrid Sketching for -Embeddings
Neophytos Charalambides, Arya Mazumdar
Linear algebraic operations are ubiquitous in engineering applications, and arise often in a variety of fields including statistical signal processing and machine learning. With co…
Iterative Sketching for Secure Coded Regression
Neophytos Charalambides, Hessam Mahdavifar, Mert Pilanci +1
Linear regression is a fundamental and primitive problem in supervised machine learning, with applications ranging from epidemiology to finance. In this work, we propose methods fo…
Gradient Coding with Iterative Block Leverage Score Sampling
Neophytos Charalambides, Mert Pilanci, Alfred Hero
We generalize the leverage score sampling sketch for -subspace embeddings, to accommodate sampling subsets of the transformed data, so that the sketching approach is approp…
Graph Sparsification by Approximate Matrix Multiplication
Neophytos Charalambides, Alfred O. Hero
Graphs arising in statistical problems, signal processing, large networks, combinatorial optimization, and data analysis are often dense, which causes both computational and storag…
Securely Aggregated Coded Matrix Inversion
Neophytos Charalambides, Mert Pilanci, Alfred Hero
Coded computing is a method for mitigating straggling workers in a centralized computing network, by using erasure-coding techniques. Federated learning is a decentralized model fo…
Secure Linear MDS Coded Matrix Inversion
Neophytos Charalambides, Mert Pilanci, Alfred Hero
A cumbersome operation in many scientific fields, is inverting large full-rank matrices. In this paper, we propose a coded computing approach for recovering matrix inverse approxim…
Orthonormal Sketches for Secure Coded Regression
Neophytos Charalambides, Hessam Mahdavifar, Mert Pilanci +1
In this work, we propose a method for speeding up linear regression distributively, while ensuring security. We leverage randomized sketching techniques, and improve straggler resi…
Generalized Fractional Repetition Codes for Binary Coded Computations
Neophytos Charalambides, Hessam Mahdavifar, Alfred O. Hero
This paper addresses the gradient coding and coded matrix multiplication problems in distributed optimization and coded computing. We present a numerically stable binary coding met…
Approximate Weighted Coded Matrix Multiplication
Neophytos Charalambides, Mert Pilanci, Alfred Hero
One of the most common, but at the same time expensive operations in linear algebra, is multiplying two matrices and . With the rapid development of machine learning and inc…
Dimensionality Reduction for -means Clustering
Neophytos Charalambides
We present a study on how to effectively reduce the dimensions of the -means clustering problem, so that provably accurate approximations are obtained. Four algorithms are prese…
Beyond the Guruswami-Sudan (and Parvaresh-Vardy) Radii: Folded Reed-Solomon, Multiplicity and Derivative Codes
Neophytos Charalambides
The classical family of Reed-Solomon codes consist of evaluations of polynomials over the finite field of degree less than , at distinct field elements. These…
Straggler Robust Distributed Matrix Inverse Approximation
Neophytos Charalambides, Mert Pilanci, Alfred O. Hero
A cumbersome operation in numerical analysis and linear algebra, optimization, machine learning and engineering algorithms; is inverting large full-rank matrices which appears in v…
Weighted Gradient Coding with Leverage Score Sampling
Neophytos Charalambides, Mert Pilanci, Alfred O. Hero
A major hurdle in machine learning is scalability to massive datasets. Approaches to overcome this hurdle include compression of the data matrix and distributing the computations.…
Numerically Stable Binary Gradient Coding
Neophytos Charalambides, Hessam Mahdavifar, Alfred O. Hero
A major hurdle in machine learning is scalability to massive datasets. One approach to overcoming this is to distribute the computational tasks among several workers. \textit{Gradi…
Isometries and Equivalences Between Point Configurations, Extended To -diffeomorphisms
Neophytos Charalambides, Steven B. Damelin, Bradley Swartz
In this announcement, we deal with the Orthogonal Procrustes Problem, in which two point configurations are compared in order to construct a map to optimally align the two sets. Th…