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Description:
We study the problem of testing whether a function f:ℝⁿ → ℝ is linear (i.e., both additive and homogeneous) in the distribution-free property testing model, where the distance between functions is measured with respect to an unknown probability distribution over ℝⁿ. We show that, given query access to f, sampling access to the unknown distribution as well as the standard Gaussian, and ε > 0, we can distinguish additive functions from functions that are ε-far from additive functions with O((1/ε)log(1/ε)) queries, independent of n. Furthermore, under the assumption that f is a continuous function, the additivity tester can be extended to a distribution-free tester for linearity using the same number of queries. On the other hand, we show that if we are only allowed to get values of f on sampled points, then any distribution-free tester requires Ω(n) samples, even if the underlying distribution is the standard Gaussian.