English

Bounding the Sensitivity of Polynomial Threshold Functions

Computational Complexity 2014-03-28 v4 Machine Learning

Abstract

We give the first non-trivial upper bounds on the average sensitivity and noise sensitivity of polynomial threshold functions. More specifically, for a Boolean function f on n variables equal to the sign of a real, multivariate polynomial of total degree d we prove 1) The average sensitivity of f is at most O(n^{1-1/(4d+6)}) (we also give a combinatorial proof of the bound O(n^{1-1/2^d}). 2) The noise sensitivity of f with noise rate \delta is at most O(\delta^{1/(4d+6)}). Previously, only bounds for the linear case were known. Along the way we show new structural theorems about random restrictions of polynomial threshold functions obtained via hypercontractivity. These structural results may be of independent interest as they provide a generic template for transforming problems related to polynomial threshold functions defined on the Boolean hypercube to polynomial threshold functions defined in Gaussian space.

Keywords

Cite

@article{arxiv.0909.5175,
  title  = {Bounding the Sensitivity of Polynomial Threshold Functions},
  author = {Prahladh Harsha and Adam Klivans and Raghu Meka},
  journal= {arXiv preprint arXiv:0909.5175},
  year   = {2014}
}

Comments

Fixed an important flaw. Some proofs are simplified from last version

R2 v1 2026-06-21T13:51:36.113Z