License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.CCC.2019.2
URN: urn:nbn:de:0030-drops-108249
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Bshouty, Nader H.

Almost Optimal Distribution-Free Junta Testing

LIPIcs-CCC-2019-2.pdf (0.5 MB)


We consider the problem of testing whether an unknown n-variable Boolean function is a k-junta in the distribution-free property testing model, where the distance between functions is measured with respect to an arbitrary and unknown probability distribution over {0,1}^n. Chen, Liu, Servedio, Sheng and Xie [Zhengyang Liu et al., 2018] showed that the distribution-free k-junta testing can be performed, with one-sided error, by an adaptive algorithm that makes O~(k^2)/epsilon queries. In this paper, we give a simple two-sided error adaptive algorithm that makes O~(k/epsilon) queries.

BibTeX - Entry

  author =	{Nader H. Bshouty},
  title =	{{Almost Optimal Distribution-Free Junta Testing}},
  booktitle =	{34th Computational Complexity Conference (CCC 2019)},
  pages =	{2:1--2:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-116-0},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{137},
  editor =	{Amir Shpilka},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-108249},
  doi =		{10.4230/LIPIcs.CCC.2019.2},
  annote =	{Keywords: Distribution-free property testing, k-Junta}

Keywords: Distribution-free property testing, k-Junta
Collection: 34th Computational Complexity Conference (CCC 2019)
Issue Date: 2019
Date of publication: 16.07.2019

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