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In the noisy population recovery problem of Dvir et al. [6], the goal is to learn an unknown distribution f on binary strings of length n from noisy samples. A noisy sample with parameter μ ∈ [0,1] is generated by selecting a sample from f, and independently flipping each coordinate of the sample with probability (1-μ)/2. We assume an(More)
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