# Testing Independence of Exchangeable Random Variables

arXiv: https://arxiv.org/abs/2210.12392 · HTML: https://mlsanity.com/paper/2210.12392/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 22 Oct 2022.

## Builds on (most cited first, 3 of 3)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [We Need to Talk About Random Splits](https://mlsanity.com/paper/2005.00636/) (2020)
- [Sequential Learning Of Neural Networks for Prequential MDL](https://mlsanity.com/paper/2210.07931/) (2022)

## Built on by (most cited first, 2 of 2)

- [The Cake that is Intelligence and Who Gets to Bake it: An AI Analogy and its Implications for Participation](https://mlsanity.com/paper/2502.03038/) (2025)
- [The Signal in the Noise: OOD Detection Through Goodness-of-Fit Testing in Factorised Latent Spaces](https://mlsanity.com/paper/2605.22496/) (2026)

## Abstract (arXiv)

Given well-shuffled data, can we determine whether the data items are statistically (in)dependent? Formally, we consider the problem of testing whether a set of exchangeable random variables are independent. We will show that this is possible and develop tests that can confidently reject the null hypothesis that data is independent and identically distributed and have high power for (some) exchangeable distributions. We will make no structural assumptions on the underlying sample space. One potential application is in Deep Learning, where data is often scraped from the whole internet, with duplications abound, which can render data non-iid and test-set evaluation prone to give wrong answers.