# Gaussian Process Neural Additive Models

arXiv: https://arxiv.org/abs/2402.12518 · HTML: https://mlsanity.com/paper/2402.12518/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Deep Neural Networks as Gaussian Processes](https://mlsanity.com/paper/1711.00165/) (2017)
- [Neural Additive Models: Interpretable Machine Learning with Neural Nets](https://mlsanity.com/paper/2004.13912/) (2020)
- [InterpretML: A Unified Framework for Machine Learning Interpretability](https://mlsanity.com/paper/1909.09223/) (2019)
- [Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data](https://mlsanity.com/paper/1909.06312/) (2019)
- [NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning](https://mlsanity.com/paper/2106.01613/) (2021)
- [Global Explanations of Neural Networks: Mapping the Landscape of Predictions](https://mlsanity.com/paper/1902.02384/) (2019)

## Built on by (most cited first, 11 of 11)

- [Sparse Variational Student-t Processes for Heavy-tailed Modeling](https://mlsanity.com/paper/2408.06699/) (2024)
- [ProtoNAM: Prototypical Neural Additive Models for Interpretable Deep Tabular Learning](https://mlsanity.com/paper/2410.04723/) (2024)
- [From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation](https://mlsanity.com/paper/2502.10540/) (2025)
- [Provably Explaining Neural Additive Models](https://mlsanity.com/paper/2602.17530/) (2026)
- [Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problems](https://mlsanity.com/paper/2411.09393/) (2024)
- [Gaussian Process Tilted Nonparametric Density Estimation using Fisher Divergence Score Matching](https://mlsanity.com/paper/2504.03485/) (2025)
- [Tabular Diffusion Counterfactual Explanations](https://mlsanity.com/paper/2509.00876/) (2025)
- [An Explainable Gaussian Process Auto-encoder for Tabular Data](https://mlsanity.com/paper/2509.00884/) (2025)
- [Layer-Wise Modality Decomposition for Interpretable Multimodal Sensor Fusion](https://mlsanity.com/paper/2511.00859/) (2025)
- [Neural Additive Experts: Context-Gated Experts for Controllable Model Additivity](https://mlsanity.com/paper/2602.10585/) (2026)
- [Beyond False Discovery Rate: A Stepdown Group SLOPE Approach for Grouped Variable Selection](https://mlsanity.com/paper/2603.00955/) (2026)

## Abstract (arXiv)

Deep neural networks have revolutionized many fields, but their black-box nature also occasionally prevents their wider adoption in fields such as healthcare and finance, where interpretable and explainable models are required. The recent development of Neural Additive Models (NAMs) is a significant step in the direction of interpretable deep learning for tabular datasets. In this paper, we propose a new subclass of NAMs that use a single-layer neural network construction of the Gaussian process via random Fourier features, which we call Gaussian Process Neural Additive Models (GP-NAM). GP-NAMs have the advantage of a convex objective function and number of trainable parameters that grows linearly with feature dimensionality. It suffers no loss in performance compared to deeper NAM approaches because GPs are well-suited for learning complex non-parametric univariate functions. We demonstrate the performance of GP-NAM on several tabular datasets, showing that it achieves comparable or better performance in both classification and regression tasks with a large reduction in the number of parameters.