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Documents authored by Adar, Tomer

Document
RANDOM
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2024.45
Refining the Adaptivity Notion in the Huge Object Model

Authors: Tomer Adar and Eldar Fischer

Published in: LIPIcs, Volume 317, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024)

Abstract
The Huge Object model for distribution testing, first defined by Goldreich and Ron in 2022, combines the features of classical string testing and distribution testing. In this model we are given access to independent samples from an unknown distribution P over the set of strings {0,1}ⁿ, but are only allowed to query a few bits from the samples. The distinction between adaptive and non-adaptive algorithms, which occurs naturally in the realm of string testing (while being irrelevant for classical distribution testing), plays a substantial role also in the Huge Object model. In this work we show that the full picture in the Huge Object model is much richer than just that of the adaptive vs. non-adaptive dichotomy. We define and investigate several models of adaptivity that lie between the fully-adaptive and the completely non-adaptive extremes. These models are naturally grounded by observing the querying process from each sample independently, and considering the "algorithmic flow" between them. For example, if we allow no information at all to cross over between samples (up to the final decision), then we obtain the locally bounded adaptive model, arguably the "least adaptive" one apart from being completely non-adaptive. A slightly stronger model allows only a "one-way" information flow. Even stronger (but still far from being fully adaptive) models follow by taking inspiration from the setting of streaming algorithms. To show that we indeed have a hierarchy, we prove a chain of exponential separations encompassing most of the models that we define.
Cite as

Tomer Adar and Eldar Fischer. Refining the Adaptivity Notion in the Huge Object Model. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 317, pp. 45:1-45:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{adar_et_al:LIPIcs.APPROX/RANDOM.2024.45,
  author =	{Adar, Tomer and Fischer, Eldar},
  title =	{{Refining the Adaptivity Notion in the Huge Object Model}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024)},
  pages =	{45:1--45:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-348-5},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{317},
  editor =	{Kumar, Amit and Ron-Zewi, Noga},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2024.45},
  URN =		{urn:nbn:de:0030-drops-210383},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2024.45},
  annote =	{Keywords: Huge-Object model, Property Testing}
}
Document
RANDOM
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2024.46
Support Testing in the Huge Object Model

Authors: Tomer Adar, Eldar Fischer, and Amit Levi

Published in: LIPIcs, Volume 317, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024)

Abstract
The Huge Object model is a distribution testing model in which we are given access to independent samples from an unknown distribution over the set of strings {0,1}ⁿ, but are only allowed to query a few bits from the samples. We investigate the problem of testing whether a distribution is supported on m elements in this model. It turns out that the behavior of this property is surprisingly intricate, especially when also considering the question of adaptivity. We prove lower and upper bounds for both adaptive and non-adaptive algorithms in the one-sided and two-sided error regime. Our bounds are tight when m is fixed to a constant (and the distance parameter ε is the only variable). For the general case, our bounds are at most O(log m) apart. In particular, our results show a surprising O(log ε^{-1}) gap between the number of queries required for non-adaptive testing as compared to adaptive testing. For one-sided error testing, we also show that an O(log m) gap between the number of samples and the number of queries is necessary. Our results utilize a wide variety of combinatorial and probabilistic methods.
Cite as

Tomer Adar, Eldar Fischer, and Amit Levi. Support Testing in the Huge Object Model. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 317, pp. 46:1-46:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{adar_et_al:LIPIcs.APPROX/RANDOM.2024.46,
  author =	{Adar, Tomer and Fischer, Eldar and Levi, Amit},
  title =	{{Support Testing in the Huge Object Model}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024)},
  pages =	{46:1--46:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-348-5},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{317},
  editor =	{Kumar, Amit and Ron-Zewi, Noga},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2024.46},
  URN =		{urn:nbn:de:0030-drops-210399},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2024.46},
  annote =	{Keywords: Huge-Object model, Property Testing}
}
Document
RANDOM
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2024.48
Improved Bounds for High-Dimensional Equivalence and Product Testing Using Subcube Queries

Authors: Tomer Adar, Eldar Fischer, and Amit Levi

Published in: LIPIcs, Volume 317, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024)

Abstract
We study property testing in the subcube conditional model introduced by Bhattacharyya and Chakraborty (2017). We obtain the first equivalence test for n-dimensional distributions that is quasi-linear in n, improving the previously known Õ(n²/ε²) query complexity bound to Õ(n/ε²). We extend this result to general finite alphabets with logarithmic cost in the alphabet size. By exploiting the specific structure of the queries that we use (which are more restrictive than general subcube queries), we obtain a cubic improvement over the best known test for distributions over {1,…,N} under the interval querying model of Canonne, Ron and Servedio (2015), attaining a query complexity of Õ((log N)/ε²), which for fixed ε almost matches the known lower bound of Ω((log N)/log log N). We also derive a product test for n-dimensional distributions with Õ(n/ε²) queries, and provide an Ω(√n/ε²) lower bound for this property.
Cite as

Tomer Adar, Eldar Fischer, and Amit Levi. Improved Bounds for High-Dimensional Equivalence and Product Testing Using Subcube Queries. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 317, pp. 48:1-48:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

Copy BibTex To Clipboard

@InProceedings{adar_et_al:LIPIcs.APPROX/RANDOM.2024.48,
  author =	{Adar, Tomer and Fischer, Eldar and Levi, Amit},
  title =	{{Improved Bounds for High-Dimensional Equivalence and Product Testing Using Subcube Queries}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024)},
  pages =	{48:1--48:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-348-5},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{317},
  editor =	{Kumar, Amit and Ron-Zewi, Noga},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2024.48},
  URN =		{urn:nbn:de:0030-drops-210418},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2024.48},
  annote =	{Keywords: Distribution testing, conditional sampling, sub-cube sampling}
}
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