2 citations · 3 across the 9 of their papers we have counts for
5 papers · 1 filter
Approximation Preserving Coresets
Milind Prabhu, Chris Schwiegelshohn, Sudarshan Shyam
Clustering in a big data setting is an intensively studied problem, with coresets emerging as one of the important paradigms in this line of work. Given a cost function $\text{cost…
Simple and Optimal Sublinear Algorithms for Mean Estimation
Beatrice Bertolotti, Matteo Russo, Chris Schwiegelshohn +1
We study the sublinear multivariate mean estimation problem in -dimensional Euclidean space. Specifically, we aim to find the mean of a ground point set , which minimizes…
Adaptive Out-Orientations with Applications
Chandra Chekuri, Aleksander Bjørn Christiansen, Jacob Holm +4
We give improved algorithms for maintaining edge-orientations of a fully-dynamic graph, such that the out-degree of each vertex is bounded. On one hand, we show how to orient the e…
Sparse Dimensionality Reduction Revisited
Mikael Møller Høgsgaard, Lion Kamma, Kasper Green Larsen +2
The sparse Johnson-Lindenstrauss transform is one of the central techniques in dimensionality reduction. It supports embedding a set of points in into $m=O(\vare…
Adaptive Out-Orientations with Applications
Aleksander B. G. Christiansen, Jacob Holm, Ivor van der Hoog +2
We give improved algorithms for maintaining edge-orientations of a fully-dynamic graph, such that the out-degree of each vertex is bounded. On one hand, we show how to orient the e…