Improved Bounds for High-Dimensional Equivalence and Product Testing using Subcube Queries

TL;DR

This paper introduces more efficient algorithms for high-dimensional distribution testing using subcube queries, significantly reducing query complexity and nearly matching theoretical lower bounds.

Contribution

It presents the first quasi-linear equivalence test for n-dimensional distributions and a new product test with improved query complexity in the subcube query model.

Findings

Quasi-linear in n equivalence test for distributions

Almost optimal query complexity for interval queries

New product test with improved bounds

Abstract

We study property testing in the subcube conditional model introduced by Bhattacharyya and Chakraborty (2017). We obtain the first equivalence test for $n$-dimensional distributions that is quasi-linear in $n$, improving the previously known $\tilde{O}(n^2/\varepsilon^2)$ query complexity bound to $\tilde{O}(n/\varepsilon^2)$. We extend this result to general finite alphabets with logarithmic cost in the alphabet size. By exploiting the specific structure of the queries that we use (which are more restrictive than general subcube queries), we obtain a cubic improvement over the best known test for distributions over $\{1,\ldots,N\}$ under the interval querying model of Canonne, Ron and Servedio (2015), attaining a query complexity of $\tilde{O}((\log N)/\varepsilon^2)$, which for fixed $\varepsilon$ almost matches the known lower bound of $\Omega((\log N)/\log\log N)$. We also derive a product test for $n$-dimensional distributions with $\tilde{O}(n / \varepsilon^2)$ queries, and provide an $\Omega(\sqrt{n} / \varepsilon^2)$ lower bound for this property.