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DOI: 10.4230/LIPIcs.APPROX/RANDOM.2024.38

Approximating the Number of Relevant Variables in a Parity Implies Proper Learning

Authors: Nader H. Bshouty and George Haddad

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

Abstract

Consider the model where we can access a parity function through random uniform labeled examples in the presence of random classification noise. In this paper, we show that approximating the number of relevant variables in the parity function is as hard as properly learning parities. More specifically, let γ:ℝ^+ → ℝ^+, where γ(x) ≥ x, be any strictly increasing function. In our first result, we show that from any polynomial-time algorithm that returns a γ-approximation, D (i.e., γ^{-1}(d(f)) ≤ D ≤ γ(d(f))), of the number of relevant variables d(f) for any parity f, we can, in polynomial time, construct a solution to the long-standing open problem of polynomial-time learning k(n)-sparse parities (parities with k(n) ≤ n relevant variables), where k(n) = ω_n(1). In our second result, we show that from any T(n)-time algorithm that, for any parity f, returns a γ-approximation of the number of relevant variables d(f) of f, we can, in polynomial time, construct a poly(Γ(n))T(Γ(n)²)-time algorithm that properly learns parities, where Γ(x) = γ(γ(x)). If T(Γ(n)²) = exp({o(n/log n)}), this would resolve another long-standing open problem of properly learning parities in the presence of random classification noise in time exp(o(n/log n)).

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Nader H. Bshouty and George Haddad. Approximating the Number of Relevant Variables in a Parity Implies Proper Learning. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 317, pp. 38:1-38:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{bshouty_et_al:LIPIcs.APPROX/RANDOM.2024.38,
  author =	{Bshouty, Nader H. and Haddad, George},
  title =	{{Approximating the Number of Relevant Variables in a Parity Implies Proper Learning}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024)},
  pages =	{38:1--38:15},
  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.2024.38},
  URN =		{urn:nbn:de:0030-drops-210316},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2024.38},
  annote =	{Keywords: PAC Learning, Random Classification Noise, Uniform Distribution, Parity, Sparcity Approximation}
}

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Approximating the Number of Relevant Variables in a Parity Implies Proper Learning

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