4 Search Results for "Freilich, Adam"

Document
RANDOM
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2024.37
Nearly Optimal Bounds for Sample-Based Testing and Learning of k-Monotone Functions

Authors: Hadley Black

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

Abstract
We study monotonicity testing of functions f : {0,1}^d → {0,1} using sample-based algorithms, which are only allowed to observe the value of f on points drawn independently from the uniform distribution. A classic result by Bshouty-Tamon (J. ACM 1996) proved that monotone functions can be learned with exp(Õ(min{(1/ε)√d,d})) samples and it is not hard to show that this bound extends to testing. Prior to our work the only lower bound for this problem was Ω(√{exp(d)/ε}) in the small ε parameter regime, when ε = O(d^{-3/2}), due to Goldreich-Goldwasser-Lehman-Ron-Samorodnitsky (Combinatorica 2000). Thus, the sample complexity of monotonicity testing was wide open for ε ≫ d^{-3/2}. We resolve this question, obtaining a nearly tight lower bound of exp(Ω(min{(1/ε)√d,d})) for all ε at most a sufficiently small constant. In fact, we prove a much more general result, showing that the sample complexity of k-monotonicity testing and learning for functions f : {0,1}^d → [r] is exp(Ω(min{(rk/ε)√d,d})). For testing with one-sided error we show that the sample complexity is exp(Ω(d)). Beyond the hypercube, we prove nearly tight bounds (up to polylog factors of d,k,r,1/ε in the exponent) of exp(Θ̃(min{(rk/ε)√d,d})) on the sample complexity of testing and learning measurable k-monotone functions f : ℝ^d → [r] under product distributions. Our upper bound improves upon the previous bound of exp(Õ(min{(k/ε²)√d,d})) by Harms-Yoshida (ICALP 2022) for Boolean functions (r = 2).
Cite as

Hadley Black. Nearly Optimal Bounds for Sample-Based Testing and Learning of k-Monotone Functions. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 317, pp. 37:1-37:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{black:LIPIcs.APPROX/RANDOM.2024.37,
  author =	{Black, Hadley},
  title =	{{Nearly Optimal Bounds for Sample-Based Testing and Learning of k-Monotone Functions}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024)},
  pages =	{37:1--37:23},
  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.2024.37},
  URN =		{urn:nbn:de:0030-drops-210308},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2024.37},
  annote =	{Keywords: Property testing, learning, Boolean functions, monotonicity, k-monotonicity}
}
Document
RANDOM
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2024.52
Trace Reconstruction from Local Statistical Queries

Authors: Xi Chen, Anindya De, Chin Ho Lee, and Rocco A. Servedio

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

Abstract
The goal of trace reconstruction is to reconstruct an unknown n-bit string x given only independent random traces of x, where a random trace of x is obtained by passing x through a deletion channel. A Statistical Query (SQ) algorithm for trace reconstruction is an algorithm which can only access statistical information about the distribution of random traces of x rather than individual traces themselves. Such an algorithm is said to be 𝓁-local if each of its statistical queries corresponds to an 𝓁-junta function over some block of 𝓁 consecutive bits in the trace. Since several - but not all - known algorithms for trace reconstruction fall under the local statistical query paradigm, it is interesting to understand the abilities and limitations of local SQ algorithms for trace reconstruction. In this paper we establish nearly-matching upper and lower bounds on local Statistical Query algorithms for both worst-case and average-case trace reconstruction. For the worst-case problem, we show that there is an Õ(n^{1/5})-local SQ algorithm that makes all its queries with tolerance τ ≥ 2^{-Õ(n^{1/5})}, and also that any Õ(n^{1/5})-local SQ algorithm must make some query with tolerance τ ≤ 2^{-Ω̃(n^{1/5})}. For the average-case problem, we show that there is an O(log n)-local SQ algorithm that makes all its queries with tolerance τ ≥ 1/poly(n), and also that any O(log n)-local SQ algorithm must make some query with tolerance τ ≤ 1/poly(n).
Cite as

Xi Chen, Anindya De, Chin Ho Lee, and Rocco A. Servedio. Trace Reconstruction from Local Statistical Queries. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 317, pp. 52:1-52:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{chen_et_al:LIPIcs.APPROX/RANDOM.2024.52,
  author =	{Chen, Xi and De, Anindya and Lee, Chin Ho and Servedio, Rocco A.},
  title =	{{Trace Reconstruction from Local Statistical Queries}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024)},
  pages =	{52:1--52:24},
  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.2024.52},
  URN =		{urn:nbn:de:0030-drops-210459},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2024.52},
  annote =	{Keywords: trace reconstruction, statistical queries, algorithmic statistics}
}
Document
DOI: 10.4230/LIPIcs.CONCUR.2024.21
Reversible Transducers over Infinite Words

Authors: Luc Dartois, Paul Gastin, Loïc Germerie Guizouarn, R. Govind, and Shankaranarayanan Krishna

Published in: LIPIcs, Volume 311, 35th International Conference on Concurrency Theory (CONCUR 2024)

Abstract
Deterministic two-way transducers capture the class of regular functions. The efficiency of composing two-way transducers has a direct implication in algorithmic problems related to synthesis, where transformation specifications are converted into equivalent transducers. These specifications are presented in a modular way, and composing the resultant machines simulates the full specification. An important result by Dartois et al. [Luc Dartois et al., 2017] shows that composition of two-way transducers enjoy a polynomial composition when the underlying transducer is reversible, that is, if they are both deterministic and co-deterministic. This is a major improvement over general deterministic two-way transducers, for which composition causes a doubly exponential blow-up in the size of the inputs in general. Moreover, they show that reversible two-way transducers have the same expressiveness as deterministic two-way transducers. However, the notion of reversible two-way transducers over infinite words as well as the question of their expressiveness were not studied yet. In this article, we introduce the class of reversible two-way transducers over infinite words and show that they enjoy the same expressive power as deterministic two-way transducers over infinite words. This is done through a non-trivial, effective construction inducing a single exponential blow-up in the set of states. Further, we also prove that composing two reversible two-way transducers over infinite words incurs only a polynomial complexity, thereby providing an efficient procedure for composition of transducers over infinite words.
Cite as

Luc Dartois, Paul Gastin, Loïc Germerie Guizouarn, R. Govind, and Shankaranarayanan Krishna. Reversible Transducers over Infinite Words. In 35th International Conference on Concurrency Theory (CONCUR 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 311, pp. 21:1-21:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{dartois_et_al:LIPIcs.CONCUR.2024.21,
  author =	{Dartois, Luc and Gastin, Paul and Germerie Guizouarn, Lo\"{i}c and Govind, R. and Krishna, Shankaranarayanan},
  title =	{{Reversible Transducers over Infinite Words}},
  booktitle =	{35th International Conference on Concurrency Theory (CONCUR 2024)},
  pages =	{21:1--21:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-339-3},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{311},
  editor =	{Majumdar, Rupak and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CONCUR.2024.21},
  URN =		{urn:nbn:de:0030-drops-207932},
  doi =		{10.4230/LIPIcs.CONCUR.2024.21},
  annote =	{Keywords: Transducers, Regular functions, Reversibility, Composition, SSTs}
}
Document
DOI: 10.4230/LIPIcs.APPROX-RANDOM.2017.37
Sample-Based High-Dimensional Convexity Testing

Authors: Xi Chen, Adam Freilich, Rocco A. Servedio, and Timothy Sun

Published in: LIPIcs, Volume 81, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2017)

Abstract
In the problem of high-dimensional convexity testing, there is an unknown set S in the n-dimensional Euclidean space which is promised to be either convex or c-far from every convex body with respect to the standard multivariate normal distribution. The job of a testing algorithm is then to distinguish between these two cases while making as few inspections of the set S as possible. In this work we consider sample-based testing algorithms, in which the testing algorithm only has access to labeled samples (x,S(x)) where each x is independently drawn from the normal distribution. We give nearly matching sample complexity upper and lower bounds for both one-sided and two-sided convexity testing algorithms in this framework. For constant c, our results show that the sample complexity of one-sided convexity testing is exponential in n, while for two-sided convexity testing it is exponential in the square root of n.
Cite as

Xi Chen, Adam Freilich, Rocco A. Servedio, and Timothy Sun. Sample-Based High-Dimensional Convexity Testing. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 81, pp. 37:1-37:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{chen_et_al:LIPIcs.APPROX-RANDOM.2017.37,
  author =	{Chen, Xi and Freilich, Adam and Servedio, Rocco A. and Sun, Timothy},
  title =	{{Sample-Based High-Dimensional Convexity Testing}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2017)},
  pages =	{37:1--37:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-044-6},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{81},
  editor =	{Jansen, Klaus and Rolim, Jos\'{e} D. P. and Williamson, David P. and Vempala, Santosh S.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2017.37},
  URN =		{urn:nbn:de:0030-drops-75867},
  doi =		{10.4230/LIPIcs.APPROX-RANDOM.2017.37},
  annote =	{Keywords: Property testing, convexity, sample-based testing}
}
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