Optimal Prediction-Augmented Algorithms for Testing Independence of Distributions
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 $ε$-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 framewor...
| Authors | Maryam Aliakbarpour, Alireza Azizi, Ria Stevens |
|---|---|
| Primary category | stat.ML |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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