2 Search Results for "Kushnir, Nick"

Document
Invited Talk
DOI: 10.4230/LIPIcs.ICALP.2025.2
Let’s Try to Be More Tolerant: On Tolerant Property Testing and Distance Approximation (Invited Talk)

Authors: Dana Ron

Published in: LIPIcs, Volume 334, 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)

Abstract
This short paper accompanies an invited talk given at ICALP2025. It is an informal, high-level presentation of tolerant testing and distance approximation. It includes some general results as well as a few specific ones, with the aim of providing a taste of this research direction within the area of sublinear algorithms.
Cite as

Dana Ron. Let’s Try to Be More Tolerant: On Tolerant Property Testing and Distance Approximation (Invited Talk). In 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 334, pp. 2:1-2:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{ron:LIPIcs.ICALP.2025.2,
  author =	{Ron, Dana},
  title =	{{Let’s Try to Be More Tolerant: On Tolerant Property Testing and Distance Approximation}},
  booktitle =	{52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)},
  pages =	{2:1--2:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-372-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{334},
  editor =	{Censor-Hillel, Keren and Grandoni, Fabrizio and Ouaknine, Jo\"{e}l and Puppis, Gabriele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2025.2},
  URN =		{urn:nbn:de:0030-drops-233798},
  doi =		{10.4230/LIPIcs.ICALP.2025.2},
  annote =	{Keywords: Sublinear Algorithms, Tolerant Property Testing, Distance Approximation}
}
Document
RANDOM
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2023.46
Testing Versus Estimation of Graph Properties, Revisited

Authors: Lior Gishboliner, Nick Kushnir, and Asaf Shapira

Published in: LIPIcs, Volume 275, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023)

Abstract
A graph G on n vertices is ε-far from property P if one should add/delete at least ε n² edges to turn G into a graph satisfying P. A distance estimator for P is an algorithm that given G and α, ε > 0 distinguishes between the case that G is (α-ε)-close to 𝒫 and the case that G is α-far from 𝒫. If P has a distance estimator whose query complexity depends only on ε, then P is said to be estimable. Every estimable property is clearly also testable, since testing corresponds to estimating with α = ε. A central result in the area of property testing is the Fischer-Newman theorem, stating that an inverse statement also holds, that is, that every testable property is in fact estimable. The proof of Fischer and Newmann was highly ineffective, since it incurred a tower-type loss when transforming a testing algorithm for P into a distance estimator. This raised the natural problem, studied recently by Fiat-Ron and by Hoppen-Kohayakawa-Lang-Lefmann-Stagni, whether one can find a transformation with a polynomial loss. We obtain the following results. - We show that if P is hereditary, then one can turn a tester for P into a distance estimator with an exponential loss. This is an exponential improvement over the result of Hoppen et. al., who obtained a transformation with a double exponential loss. - We show that for every P, one can turn a testing algorithm for P into a distance estimator with a double exponential loss. This improves over the transformation of Fischer-Newman that incurred a tower-type loss. Our main conceptual contribution in this work is that we manage to turn the approach of Fischer-Newman, which was inherently ineffective, into an efficient one. On the technical level, our main contribution is in establishing certain properties of Frieze-Kannan Weak Regular partitions that are of independent interest.
Cite as

Lior Gishboliner, Nick Kushnir, and Asaf Shapira. Testing Versus Estimation of Graph Properties, Revisited. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 275, pp. 46:1-46:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{gishboliner_et_al:LIPIcs.APPROX/RANDOM.2023.46,
  author =	{Gishboliner, Lior and Kushnir, Nick and Shapira, Asaf},
  title =	{{Testing Versus Estimation of Graph Properties, Revisited}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023)},
  pages =	{46:1--46:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-296-9},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{275},
  editor =	{Megow, Nicole and Smith, Adam},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2023.46},
  URN =		{urn:nbn:de:0030-drops-188713},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2023.46},
  annote =	{Keywords: Testing, estimation, weak regularity, randomized algorithms, graph theory, Frieze-Kannan Regularity}
}
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