# ISDE : Independence Structure Density Estimation

arXiv: https://arxiv.org/abs/2203.09783 · HTML: https://mlsanity.com/paper/2203.09783/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 32 references that are in the map.
- First posted on arXiv 18 Mar 2022.

## Builds on (most cited first, 2 of 2)

- [Kernel Operations on the GPU, with Autodiff, without Memory Overflows](https://mlsanity.com/paper/2004.11127/) (2020)
- [Block-diagonal covariance selection for high-dimensional Gaussian graphical models](https://mlsanity.com/paper/1511.04033/) (2015)

## Abstract (arXiv)

In this paper, we propose ISDE (Independence Structure Density Estimation), an algorithm designed to estimate a multivariate density under Kullback-Leibler loss and the Independence Structure (IS) model. IS tackles the curse of dimensionality by separating features into independent groups. We explain the construction of ISDE and present some experiments to show its performance on synthetic and real-world data. Performance is measured quantitatively by comparing empirical log-likelihood with other density estimation methods and qualitatively by analyzing outputted partitions of variables. We also provide information about complexity and running time.