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Model-agnostic distribution recovery in high dimension via Fourier measurements
We study the problem of reconstructing an unknown probability distribution to high accuracy, given noisy information about its low-degree Fourier coefficients. Prior results for this basic problem have typically imposed strong modeling assumptions on the distribution, often requiring that it is a li
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