most citedDimensionality Reduction for -means Clustering

2 citations · 2 across the 2 of their papers we have counts for

collaborators

6 papers

cs.IT2022

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…

cs.IT2020

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…

cs.LG20202 cited

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…

cs.IT2020

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…

cs.IT2020

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.…

cs.IT2020

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…