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Jakub Tětek

4 papers here

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

author position
  • middle author1
  • last author3

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

fields
  • cs.DS3
  • cs.DC1
ORCID 0000-0002-2046-1627
same name
  • Jakub Tětek — 1 paper

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

most citedProbGraph: High-Performance and High-Accuracy Graph Mining with Probabilistic Set Representations

1 citations · 1 across the 4 of their papers we have counts for

collaborators

4 papers

cs.DS2024

Testing Identity of Distributions under Kolmogorov Distance in Polylogarithmic Space

Christian Janos Lebeda, Jakub Tětek

Suppose we have a sample from a distribution D and we want to test whether D=D∗ for a fixed distribution D∗. Specifically, we want to reject with constant probability, if…

cs.DS2022

Bias Reduction for Sum Estimation

Talya Eden, Jakob Bæk Tejs Houen, Shyam Narayanan +2

In classical statistics and distribution testing, it is often assumed that elements can be sampled from some distribution P, and that when an element x is sampled, the probabil…

cs.DC2022★ 1 cited

ProbGraph: High-Performance and High-Accuracy Graph Mining with Probabilistic Set Representations

Maciej Besta, Cesare Miglioli, Paolo Sylos Labini +11

Important graph mining problems such as Clustering are computationally demanding. To significantly accelerate these problems, we propose ProbGraph: a graph representation that enab…

cs.DS2022

A Nearly Tight Analysis of Greedy k-means++

Christoph Grunau, Ahmet Alper Özüdoğru, Václav Rozhoň +1

The famous k-means++ algorithm of Arthur and Vassilvitskii [SODA 2007] is the most popular way of solving the k-means problem in practice. The algorithm is very simple: it samp…

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