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
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2019/10824/
Go to the corresponding LIPIcs Volume Portal

Bshouty, Nader H.

Almost Optimal Distribution-Free Junta Testing

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

Abstract

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

@InProceedings{bshouty:LIPIcs:2019:10824,
  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 =		{http://drops.dagstuhl.de/opus/volltexte/2019/10824},
  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

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI