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Random Restrictions of High-Dimensional Distributions and Uniformity Testing with Subcube Conditioning

, , , , and . (2019)cite arxiv:1911.07357Comment: 40 pages.

Abstract

We give a nearly-optimal algorithm for testing uniformity of distributions supported on $\-1,1\^n$, which makes $O (n/\varepsilon^2)$ queries to a subcube conditional sampling oracle (Bhattacharyya and Chakraborty (2018)). The key technical component is a natural notion of random restriction for distributions on $\-1,1\^n$, and a quantitative analysis of how such a restriction affects the mean vector of the distribution. Along the way, we consider the problem of mean testing with independent samples and provide a nearly-optimal algorithm.

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[1911.07357] Random Restrictions of High-Dimensional Distributions and Uniformity Testing with Subcube Conditioning

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