# Dependence model assessment and selection with DecoupleNets

arXiv: https://arxiv.org/abs/2202.03406 · HTML: https://mlsanity.com/paper/2202.03406/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 7 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Generative Moment Matching Networks](https://mlsanity.com/paper/1502.02761/) (2015)
- [Training generative neural networks via Maximum Mean Discrepancy optimization](https://mlsanity.com/paper/1505.03906/) (2015)
- [Quasi-random sampling for multivariate distributions via generative neural networks](https://mlsanity.com/paper/1811.00683/) (2018)
- [Multivariate time-series modeling with generative neural networks](https://mlsanity.com/paper/2002.10645/) (2020)

## Built on by (most cited first, 1 of 1)

- [Adaptive generative moment matching networks for improved learning of dependence structures](https://mlsanity.com/paper/2508.21531/) (2025)

## Abstract (arXiv)

Neural networks are suggested for learning a map from d-dimensional samples with any underlying dependence structure to multivariate uniformity in d' dimensions. This map, termed DecoupleNet, is used for dependence model assessment and selection. If the data-generating dependence model was known, and if it was among the few analytically tractable ones, one such transformation for d'=d is Rosenblatt's transform. DecoupleNets have multiple advantages. For example, they only require an available sample and are applicable to d'<d, in particular d'=2. This allows for simpler model assessment and selection, both numerically and, because d'=2, especially graphically. A graphical assessment method has the advantage of being able to identify why, or in which region of the domain, a candidate model does not provide an adequate fit, thus leading to model selection in particular regions of interest or improved model building strategies in such regions. Through simulation studies with data from various copulas, the feasibility and validity of this novel DecoupleNet approach is demonstrated. Applications to real world data illustrate its usefulness for model assessment and selection.