# The Randomized Dependence Coefficient

arXiv: https://arxiv.org/abs/1304.7717 · HTML: https://mlsanity.com/paper/1304.7717/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2013.

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- [Robust Non-Linear Correlations via Polynomial Regression](https://mlsanity.com/paper/2509.09380/) (2025)
- [Fast Flow Matching based Conditional Independence Tests for Causal Discovery](https://mlsanity.com/paper/2602.08315/) (2026)
- [Kernel Integrated R2: A Measure of Dependence](https://mlsanity.com/paper/2602.22985/) (2026)
- [Who Guards the Guardians? The Challenges of Evaluating Identifiability of Learned Representations](https://mlsanity.com/paper/2602.24278/) (2026)
- [KGroups: A Versatile Univariate Max-Relevance Min-Redundancy Feature Selection Algorithm for High-dimensional Biological Data](https://mlsanity.com/paper/2603.28417/) (2026)
- [To Learn or Not to Learn: A Litmus Test for Using Reinforcement Learning in Control](https://mlsanity.com/paper/2604.11463/) (2026)
- [Deep-testing: the case of dependence detection](https://mlsanity.com/paper/2604.26558/) (2026)
- [Conditional Inference Trees and Forests for Feature Selection](https://mlsanity.com/paper/2607.01417/) (2026)

## Abstract (arXiv)

We introduce the Randomized Dependence Coefficient (RDC), a measure of non-linear dependence between random variables of arbitrary dimension based on the Hirschfeld-Gebelein-R'enyi Maximum Correlation Coefficient. RDC is defined in terms of correlation of random non-linear copula projections; it is invariant with respect to marginal distribution transformations, has low computational cost and is easy to implement: just five lines of R code, included at the end of the paper.