Optimal Testing of Reed-Muller Codes
Arnab Bhattacharyya, Swastik Kopparty, Grant Schoenebeck, Madhu Sudan,, David Zuckerman

TL;DR
This paper provides an optimal analysis of the Gowers norm test for Reed-Muller codes, showing it effectively distinguishes functions close to degree d polynomials with a universal constant rejection probability.
Contribution
It offers a tight analysis of the Gowers norm test's rejection probability, improving understanding of its tolerance and implications for property testing.
Findings
Gowers norm test rejects functions Omega(2^{-d})-far with constant probability
The analysis yields a new understanding of the relationship between Gowers norm and polynomial correlation
Implications include improved XOR lemma parameters and a query hierarchy for affine-invariant properties
Abstract
We consider the problem of testing if a given function f : F_2^n -> F_2 is close to any degree d polynomial in n variables, also known as the Reed-Muller testing problem. The Gowers norm is based on a natural 2^{d+1}-query test for this property. Alon et al. [AKKLR05] rediscovered this test and showed that it accepts every degree d polynomial with probability 1, while it rejects functions that are Omega(1)-far with probability Omega(1/(d 2^{d})). We give an asymptotically optimal analysis of this test, and show that it rejects functions that are (even only) Omega(2^{-d})-far with Omega(1)-probability (so the rejection probability is a universal constant independent of d and n). This implies a tight relationship between the (d+1)st Gowers norm of a function and its maximal correlation with degree d polynomials, when the correlation is close to 1. Our proof works by induction on n and…
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Taxonomy
TopicsAlgorithms and Data Compression · semigroups and automata theory · Machine Learning and Algorithms
