Attribute Estimation and Testing Quasi-Symmetry

TL;DR

This paper introduces a property testing method called attribute estimation for quasi-symmetric Boolean functions, enabling efficient testing and dependency identification with probabilistic guarantees.

Contribution

It presents a novel attribute estimation approach for testing quasi-symmetry in Boolean functions, extending property testing techniques to determine relevant arguments.

Findings

The test accepts all quasi-symmetric functions.

It rejects functions far from quasi-symmetry with high probability.

The method probes functions with O((n/epsilon) log(n/delta)) queries.

Abstract

A Boolean function is symmetric if it is invariant under all permutations of its arguments; it is quasi-symmetric if it is symmetric with respect to the arguments on which it actually depends. We present a test that accepts every quasi-symmetric function and, except with an error probability at most delta>0, rejects every function that differs from every quasi-symmetric function on at least a fraction epsilon>0 of the inputs. For a function of n arguments, the test probes the function at O((n/epsilon)\log(n/delta)) inputs. Our quasi-symmetry test acquires information concerning the arguments on which the function actually depends. To do this, it employs a generalization of the property testing paradigm that we call attribute estimation. Like property testing, attribute estimation uses random sampling to obtain results that have only "one-sided'' errors and that are close to accurate with high probability.