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In the noisy population recovery problem of Dvir et al. [DRWY12], the goal is to learn an unknown distribution f on binary strings of length n from noisy samples. For some parameter µ ∈ [0, 1], a noisy sample is generated by flipping each coordinate of a sample from f independently with probability (1 − µ)/2. We assume an upper bound k on the size of the… (More)

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