Reconstruction under outliers for Fourier-sparse functions
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We consider the problem of learning an unknown f with a sparse Fourier spectrum in the presence of outlier noise. In particular, the algorithm has access to a noisy oracle for (an unknown) f such that (i) the Fourier spectrum of f is k-sparse; (ii) at any query point x, the oracle returns y such that with probability 1-ρ, |y-f(x)| <ϵ. However, with probability ρ, the error y-f(x) can be arbitrarily large. We study Fourier sparse functions over both the discrete cube {0,1}^n and the torus [0,1) and for both these domains, we design efficient algorithms which can tolerate any ρ<1/2 fraction of outliers. We note that the analogous problem for low-degree polynomials has recently been studied in several works [AK03, GZ16, KKP17] and similar algorithmic guarantees are known in that setting. While our main results pertain to the case where the location of the outliers, i.e., x such that |y-f(x)|>ϵ is randomly distributed, we also study the case where the outliers are adversarially located. In particular, we show that over the torus, assuming that the Fourier transform satisfies a certain granularity condition, there is a sample efficient algorithm to tolerate ρ =Ω(1) fraction of outliers and further, that this is not possible without such a granularity condition. Finally, while not the principal thrust, our techniques also allow us non-trivially improve on learning low-degree functions f on the hypercube in the presence of adversarial outlier noise. Our techniques combine a diverse array of tools from compressive sensing, sparse Fourier transform, chaining arguments and complex analysis.
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