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- W2975218903 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." @default.
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- W2975218903 date "2019-07-01" @default.
- W2975218903 modified "2023-09-26" @default.
- W2975218903 title "Boolean Functions with Biased Inputs: Approximation and Noise Sensitivity" @default.
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- W2975218903 doi "https://doi.org/10.1109/isit.2019.8849233" @default.
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