# Beyond Intuition, a Framework for Applying GPs to Real-World Data

arXiv: https://arxiv.org/abs/2307.03093 · HTML: https://mlsanity.com/paper/2307.03093/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 43 references that are in the map.
- First posted on arXiv 6 Jul 2023.

## Builds on (most cited first, 21 of 21)

- [GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration](https://mlsanity.com/paper/1809.11165/) (2018)
- [When Gaussian Process Meets Big Data: A Review of Scalable GPs](https://mlsanity.com/paper/1807.01065/) (2018)
- [Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)](https://mlsanity.com/paper/1503.01057/) (2015)
- [Exact Gaussian Processes on a Million Data Points](https://mlsanity.com/paper/1903.08114/) (2019)
- [Distributed Gaussian Processes](https://mlsanity.com/paper/1502.02843/) (2015)
- [The Promises and Pitfalls of Deep Kernel Learning](https://mlsanity.com/paper/2102.12108/) (2021)
- [A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization](https://mlsanity.com/paper/2111.05040/) (2021)
- [Gaussian Process Prior Variational Autoencoders](https://mlsanity.com/paper/1810.11738/) (2018)
- [Veridical Data Science](https://mlsanity.com/paper/1901.08152/) (2019)
- [A Framework for Interdomain and Multioutput Gaussian Processes](https://mlsanity.com/paper/2003.01115/) (2020)
- [Convolutional Gaussian Processes](https://mlsanity.com/paper/1709.01894/) (2017)
- [Streaming Sparse Gaussian Process Approximations](https://mlsanity.com/paper/1705.07131/) (2017)
- [Automatic Construction and Natural-Language Description of Nonparametric Regression Models](https://mlsanity.com/paper/1402.4304/) (2014)
- [Additive Gaussian Processes Revisited](https://mlsanity.com/paper/2206.09861/) (2022)
- [Kernel Identification Through Transformers](https://mlsanity.com/paper/2106.08185/) (2021)
- [BRUNO: A Deep Recurrent Model for Exchangeable Data](https://mlsanity.com/paper/1802.07535/) (2018)
- [Modeling the Machine Learning Multiverse](https://mlsanity.com/paper/2206.05985/) (2022)
- [Efficient Gaussian Neural Processes for Regression](https://mlsanity.com/paper/2108.09676/) (2021)
- [Improved prediction accuracy for disease risk mapping using Gaussian Process stacked generalisation](https://mlsanity.com/paper/1612.03278/) (2016)
- [Variational Nearest Neighbor Gaussian Process](https://mlsanity.com/paper/2202.01694/) (2022)
- [Kernel Learning for Explainable Climate Science](https://mlsanity.com/paper/2209.04947/) (2022)

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

- [Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes](https://mlsanity.com/paper/2405.18457/) (2024)

## Abstract (arXiv)

Gaussian Processes (GPs) offer an attractive method for regression over small, structured and correlated datasets. However, their deployment is hindered by computational costs and limited guidelines on how to apply GPs beyond simple low-dimensional datasets. We propose a framework to identify the suitability of GPs to a given problem and how to set up a robust and well-specified GP model. The guidelines formalise the decisions of experienced GP practitioners, with an emphasis on kernel design and options for computational scalability. The framework is then applied to a case study of glacier elevation change yielding more accurate results at test time.