# Projection predictive model selection for Gaussian processes

arXiv: https://arxiv.org/abs/1510.04813 · HTML: https://mlsanity.com/paper/1510.04813/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2015.

## Built on by (most cited first, 3 of 3)

- [Kernel-Based Sparse Additive Nonlinear Model Structure Detection through a Linearization Approach](https://mlsanity.com/paper/2508.01453/) (2025)
- [Calibrated Derivative-Process Sensitivity for Gaussian-Process Variable Selection](https://mlsanity.com/paper/2609.33549/) (2026)
- [Sparsity by Default: The Theory and Practice of ARD in Gaussian Process Regression for Variable Selection](https://mlsanity.com/paper/2609.33550/) (2026)

## Abstract (arXiv)

We propose a new method for simplification of Gaussian process (GP) models by projecting the information contained in the full encompassing model and selecting a reduced number of variables based on their predictive relevance. Our results on synthetic and real world datasets show that the proposed method improves the assessment of variable relevance compared to the automatic relevance determination (ARD) via the length-scale parameters. We expect the method to be useful for improving explainability of the models, reducing the future measurement costs and reducing the computation time for making new predictions.