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Wednesday, March 17, 2021
9:00am to 10:00am
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Junta Distance Approximation with Sub-Exponential Queries
Avishay Tal, Assistant Professor, UC Berkeley,

Abstract: Joint Work with Vishnu Iyer and Michael Whitmeyer

A Boolean function $f:{0,1}^n \to {0,1}$ is called a k-junta if it depends only on k out of the n input bits. Junta testing is the task of distinguishing between k-juntas and functions that are far from k-juntas. A long line of work settled the query complexity of testing k-juntas, which is O(k log(k)) [Blais, STOC 2009; Saglam, FOCS 2018]. Suppose, however, that f is not a perfect k-junta but rather correlated with a k-junta. How well can we estimate this correlation? This question was asked by De, Mossel, and Neeman [FOCS 2019], who gave an algorithm for the task making ~exp(k) queries. We present an improved algorithm that makes ~exp(sqrt{k}) many queries.

Along the way, we also give an algorithm, making poly(k) queries, that provides "implicit oracle access" to the underlying k-junta. Our techniques are Fourier analytical and introduce the notion of "normalized influences" that might be of independent interest.


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For more information, please contact Bonnie Leung by email at [email protected].