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20102024
most citedDiscrepancy, Coresets, and Sketches in Machine Learning

7 citations · 27 across the 8 of their papers we have counts for

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cs.DS20191 cited

Streaming Quantiles Algorithms with Small Space and Update Time

Nikita Ivkin, Edo Liberty, Kevin Lang +2

Approximating quantiles and distributions over streaming data has been studied for roughly two decades now. Recently, Karnin, Lang, and Liberty proposed the first asymptotically op…

cs.DS20171 cited

A High-Performance Algorithm for Identifying Frequent Items in Data Streams

Daniel Anderson, Pryce Bevan, Kevin Lang +3

Estimating frequencies of items over data streams is a common building block in streaming data measurement and analysis. Misra and Gries introduced their seminal algorithm for the…

cs.DS2016

Optimal Quantile Approximation in Streams

Zohar Karnin, Kevin Lang, Edo Liberty

This paper resolves one of the longest standing basic problems in the streaming computational model. Namely, optimal construction of quantile sketches. An approximate…

cs.DS2016

Efficient Frequent Directions Algorithm for Sparse Matrices

Mina Ghashami, Edo Liberty, Jeff M. Phillips

This paper describes Sparse Frequent Directions, a variant of Frequent Directions for sketching sparse matrices. It resembles the original algorithm in many ways: both receive the…

cs.DS20155 cited

Greedy Minimization of Weakly Supermodular Set Functions

Christos Boutsidis, Edo Liberty, Maxim Sviridenko

This paper defines weak--supermodularity for set functions. Many optimization objectives in machine learning and data mining seek to minimize such functions under cardinality co…

cs.DS20127 cited

Simple and Deterministic Matrix Sketching

Edo Liberty

We adapt a well known streaming algorithm for approximating item frequencies to the matrix sketching setting. The algorithm receives the rows of a large matrix $A \in \R^{n \times…