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researcher

Vincent Cohen-Addad

4 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author3
  • middle author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.DS4
same name
  • Vincent Cohen-Addad — 12 papers
  • Vincent Cohen-Addad — 6 papers, h 9
  • Vincent Cohen-Addad — 5 papers, h 5
  • Vincent Cohen-Addad — 4 papers, h 11
  • Vincent Cohen-Addad — 4 papers
  • Vincent Cohen-Addad — 3 papers, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182022
most citedTowards Optimal Lower Bounds for k-median and k-means Coresets

4 citations · 4 across the 3 of their papers we have counts for

collaborators

4 papers

cs.DS2022

The Power of Uniform Sampling for Coresets

Vladimir Braverman, Vincent Cohen-Addad, Shaofeng H. -C. Jiang +4

Motivated by practical generalizations of the classic k-median and k-means objectives, such as clustering with size constraints, fair clustering, and Wasserstein barycenter, we…

cs.DS2022★ 4 cited

Towards Optimal Lower Bounds for k-median and k-means Coresets

Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic +1

Given a set of points in a metric space, the (k,z)-clustering problem consists of finding a set of k points called centers, such that the sum of distances raised to the power o…

cs.DS2021

An Improved Local Search Algorithm for k-Median

Vincent Cohen-Addad, Anupam Gupta, Lunjia Hu +2

We present a new local-search algorithm for the k-median clustering problem. We show that local optima for this algorithm give a (2.836+ε)-approximation; our result improves up…

cs.DS2018

Near-Linear Time Approximation Schemes for Clustering in Doubling Metrics

Vincent Cohen-Addad, Andreas Emil Feldmann, David Saulpic

We consider the classic Facility Location, k-Median, and k-Means problems in metric spaces of doubling dimension d. We give nearly linear-time approximation schemes for each…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.