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Non-Adaptive Proper Learning Polynomials

Author Nader H. Bshouty

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Nader H. Bshouty
  • Department of Computer Science, Technion, Haifa, Israel

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Nader H. Bshouty. Non-Adaptive Proper Learning Polynomials. In 40th International Symposium on Theoretical Aspects of Computer Science (STACS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 254, pp. 16:1-16:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.STACS.2023.16

Abstract

We give the first polynomial-time non-adaptive proper learning algorithm of Boolean sparse multivariate polynomial under the uniform distribution. Our algorithm, for s-sparse polynomial over n variables, makes q = (s/ε)^{γ(s,ε)}log n queries where 2.66 ≤ γ(s,ε) ≤ 6.922 and runs in Õ(n)⋅ poly(s,1/ε) time. We also show that for any ε = 1/s^{O(1)} any non-adaptive learning algorithm must make at least (s/ε)^{Ω(1)}log n queries. Therefore, the query complexity of our algorithm is also polynomial in the optimal query complexity and optimal in n.

Subject Classification

ACM Subject Classification
  • Theory of computation
Keywords
  • Polynomial
  • Learning
  • Testing

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References

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