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dc.contributor.authorDe, Anindya
dc.contributor.authorMossel, Elchanan
dc.contributor.authorNeeman, Joe
dc.date.accessioned2021-11-05T15:05:03Z
dc.date.available2021-11-05T15:05:03Z
dc.date.issued2019
dc.identifier.urihttps://hdl.handle.net/1721.1/137504
dc.description.abstract© 2019 IEEE. The problem of tolerant junta testing is a natural and challenging problem which asks if the property of a function having some specified correlation with a k-Junta is testable. In this paper we give an affirmative answer to this question: There is an algorithm which given distance parameters c, d, and oracle access to a Boolean function f on the hypercube, has query complexity exp(k).poly(1/(c-d)) and distinguishes between the following cases: 1. The distance of f from any k-junta is at least c; 2. There is a k-junta g which has distance at most d from f. This is the first non-Trivial tester (i.e., query complexity is independent of the ambient dimension n) which works for all c and d (bounded by 0.5). The best previously known results by Blais et~al., required c to be at least 16d. In fact, with the same query complexity, we accomplish the stronger goal of identifying the most correlated k-junta, up to permutations of the coordinates. We can further improve the query complexity to poly(k/(c-d)) for the (weaker) task of distinguishing between the following cases: 1. The distance of f from any k'-junta is at least c. 2. There is a k-junta g which is at a distance at most d from f. Here k'=poly(k/(c-d)). Our main tools are Fourier analysis based algorithms that simulate oracle access to influential coordinates of functions.en_US
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionof10.1109/FOCS.2019.00090en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleJunta Correlation is Testableen_US
dc.typeArticleen_US
dc.identifier.citationDe, Anindya, Mossel, Elchanan and Neeman, Joe. 2019. "Junta Correlation is Testable." Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS, 2019-November.
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mathematics
dc.contributor.departmentStatistics and Data Science Center (Massachusetts Institute of Technology)
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Society
dc.relation.journalProceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCSen_US
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2021-05-25T12:05:27Z
dspace.orderedauthorsDe, A; Mossel, E; Neeman, Jen_US
dspace.date.submission2021-05-25T12:05:28Z
mit.journal.volume2019-Novemberen_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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