Optimal Prediction-Augmented Algorithms for Testing Independence of Distributions

TL;DR

This paper introduces robust, prediction-augmented algorithms for independence testing that adaptively improve sample efficiency using auxiliary information, achieving optimal complexity in both bivariate and multivariate cases.

Contribution

It presents the first adaptive independence testers that incorporate auxiliary predictions, maintaining robustness while reducing sample complexity based on prediction accuracy.

Findings

Achieves adaptive sample complexity reduction in independence testing.

Provides a multivariate independence testing framework.

Matches minimax lower bounds, proving optimality.

Abstract

Independence testing is a fundamental problem in statistical inference: given samples from a joint distribution $p$ over multiple random variables, the goal is to determine whether $p$ is a product distribution or is $\epsilon$-far from all product distributions in total variation distance. In the non-parametric finite-sample regime, this task is notoriously expensive, as the minimax sample complexity scales polynomially with the support size. In this work, we move beyond these worst-case limitations by leveraging the framework of \textit{augmented distribution testing}. We design independence testers that incorporate auxiliary, but potentially untrustworthy, predictive information. Our framework ensures that the tester remains robust, maintaining worst-case validity regardless of the prediction's quality, while significantly improving sample efficiency when the prediction is accurate. Our main contributions include: (i) a bivariate independence tester for discrete distributions that adaptively reduces sample complexity based on the prediction error; (ii) a generalization to the high-dimensional multivariate setting for testing the independence of $d$ random variables; and (iii) matching minimax lower bounds demonstrating that our testers achieve optimal sample complexity.