Boolean Functions with Biased Inputs: Approximation and Noise Sensitivity

Mohsen Heidari; S. Sandeep Pradhan; Ramji Venkataramanan

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arXiv · 1901.10576

Boolean Functions with Biased Inputs: Approximation and Noise Sensitivity

Mohsen Heidari·S. Sandeep Pradhan·Ramji Venkataramanan

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Abstract

This paper considers the problem of approximating a Boolean function $f$ using another Boolean function from a specified class. Two classes of approximating functions are considered: $k$-juntas, and linear Boolean functions. The $n$ input bits of the function are assumed to be independently drawn from a distribution that may be biased. The quality of approximation is measured by the mismatch probability between $f$ and the approximating function $g$. For each class, the optimal approximation and the associated mismatch probability is characterized in terms of the biased Fourier expansion of $f$. The technique used to analyze the mismatch probability also yields an expression for the noise sensitivity of $f$ in terms of the biased Fourier coefficients, under a general i.i.d. input perturbation model.

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Mohsen Heidari, S. Sandeep Pradhan, Ramji Venkataramanan. 2019-07-08. Boolean Functions with Biased Inputs: Approximation and Noise Sensitivity. https://arxiv.org/abs/1901.10576

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