Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier Features

TL;DR

This paper analyzes the computational-statistical trade-off in kernel two-sample testing using random Fourier features, showing that with careful parameter choices, sub-quadratic time tests can match the power of traditional methods.

Contribution

It provides a theoretical framework demonstrating how to achieve the same minimax separation rates as the MMD test with sub-quadratic complexity using random Fourier features.

Findings

Approximated MMD test is pointwise consistent only with infinite features.

Careful selection of features achieves minimax rates in sub-quadratic time.

Simulation studies confirm theoretical results.

Abstract

Recent years have seen a surge in methods for two-sample testing, among which the Maximum Mean Discrepancy (MMD) test has emerged as an effective tool for handling complex and high-dimensional data. Despite its success and widespread adoption, the primary limitation of the MMD test has been its quadratic-time complexity, which poses challenges for large-scale analysis. While various approaches have been proposed to expedite the procedure, it has been unclear whether it is possible to attain the same power guarantee as the MMD test at sub-quadratic time cost. To fill this gap, we revisit the approximated MMD test using random Fourier features, and investigate its computational-statistical trade-off. We start by revealing that the approximated MMD test is pointwise consistent in power only when the number of random features approaches infinity. We then consider the uniform power of the test and study the time-power trade-off under the minimax testing framework. Our result shows that, by carefully choosing the number of random features, it is possible to attain the same minimax separation rates as the MMD test within sub-quadratic time. We demonstrate this point under different distributional assumptions such as densities in a Sobolev ball. Our theoretical findings are corroborated by simulation studies.