| dc.contributor.author | De, Anindya | |
| dc.contributor.author | Mossel, Elchanan | |
| dc.contributor.author | Neeman, Joe | |
| dc.date.accessioned | 2021-11-05T15:05:03Z | |
| dc.date.available | 2021-11-05T15:05:03Z | |
| dc.date.issued | 2019 | |
| dc.identifier.uri | https://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.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
| dc.relation.isversionof | 10.1109/FOCS.2019.00090 | en_US |
| dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | en_US |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en_US |
| dc.source | arXiv | en_US |
| dc.title | Junta Correlation is Testable | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | De, 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.department | Massachusetts Institute of Technology. Department of Mathematics | |
| dc.contributor.department | Statistics and Data Science Center (Massachusetts Institute of Technology) | |
| dc.contributor.department | Massachusetts Institute of Technology. Institute for Data, Systems, and Society | |
| dc.relation.journal | Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS | en_US |
| dc.eprint.version | Original manuscript | en_US |
| dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
| eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
| dc.date.updated | 2021-05-25T12:05:27Z | |
| dspace.orderedauthors | De, A; Mossel, E; Neeman, J | en_US |
| dspace.date.submission | 2021-05-25T12:05:28Z | |
| mit.journal.volume | 2019-November | en_US |
| mit.license | OPEN_ACCESS_POLICY | |
| mit.metadata.status | Authority Work and Publication Information Needed | en_US |