2026-07-26T15:53:54Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:14298 2024-03-06T10:53:54Z ddc:004 open_access

Junta Distance Approximation with Sub-Exponential Queries Iyer, Vishnu Tal, Avishay Whitmeyer, Michael Algorithms Complexity Theory Fourier Analysis Juntas Normalized Influence Property Testing Tolerant Property Testing Leveraging tools of De, Mossel, and Neeman [FOCS, 2019], we show two different results pertaining to the tolerant testing of juntas. Given black-box access to a Boolean function f:{±1}ⁿ → {±1}: 1) We give a poly(k, 1/(ε)) query algorithm that distinguishes between functions that are γ-close to k-juntas and (γ+ε)-far from k'-juntas, where k' = O(k/(ε²)). 2) In the non-relaxed setting, we extend our ideas to give a 2^{Õ(√{k/ε})} (adaptive) query algorithm that distinguishes between functions that are γ-close to k-juntas and (γ+ε)-far from k-juntas. To the best of our knowledge, this is the first subexponential-in-k query algorithm for approximating the distance of f to being a k-junta (previous results of Blais, Canonne, Eden, Levi, and Ron [SODA, 2018] and De, Mossel, and Neeman [FOCS, 2019] required exponentially many queries in k). Our techniques are Fourier analytical and make use of the notion of "normalized influences" that was introduced by Talagrand [Michel Talagrand, 1994]. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Vishnu Iyer and Avishay Tal and Michael Whitmeyer 2021 Is Part Of LIPIcs, Volume 200, 36th Computational Complexity Conference (CCC 2021) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.CCC.2021.24 urn:nbn:de:0030-drops-142988 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CCC.2021.24 eng https://creativecommons.org/licenses/by/4.0/legalcode