# Neural-ANOVA: Analytical Model Decomposition using Automatic Integration

arXiv: https://arxiv.org/abs/2408.12319 · HTML: https://mlsanity.com/paper/2408.12319/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 22 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [Neural Additive Models: Interpretable Machine Learning with Neural Nets](https://mlsanity.com/paper/2004.13912/) (2020)
- [AutoInt: Automatic Integration for Fast Neural Volume Rendering](https://mlsanity.com/paper/2012.01714/) (2020)
- [NeRF: Neural Radiance Field in 3D Vision: A Comprehensive Review (Updated Post-Gaussian Splatting)](https://mlsanity.com/paper/2210.00379/) (2022)
- [Automatic Integration for Spatiotemporal Neural Point Processes](https://mlsanity.com/paper/2310.06179/) (2023)
- [Interpretable Approximation of High-Dimensional Data](https://mlsanity.com/paper/2103.13787/) (2021)
- [Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian Quadrature](https://mlsanity.com/paper/2210.17299/) (2022)
- [Physics-informed neural networks for pathloss prediction](https://mlsanity.com/paper/2211.12986/) (2022)

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

- [Common Functional Decompositions Can Mis-attribute Differences in Outcomes Between Populations](https://mlsanity.com/paper/2504.16864/) (2025)

## Abstract (arXiv)

The analysis of variance (ANOVA) decomposition offers a systematic method to understand the interaction effects that contribute to a specific decision output. In this paper we introduce Neural-ANOVA, an approach to decompose neural networks into the sum of lower-order models using the functional ANOVA decomposition. Our approach formulates a learning problem, which enables fast analytical evaluation of integrals over subspaces that appear in the calculation of the ANOVA decomposition. Finally, we conduct numerical experiments to provide insights into the approximation properties compared to other regression approaches from the literature.