# Latent Gaussian Process Regression

arXiv: https://arxiv.org/abs/1707.05534 · HTML: https://mlsanity.com/paper/1707.05534/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2017.

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

- [A Climate-Aware Deep Learning Framework for Generalizable Epidemic Forecasting](https://mlsanity.com/paper/2510.19611/) (2025)

## Abstract (arXiv)

We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of non-stationary multi-modal processes using GPs. The approach is built on extending the input space of a regression problem with a latent variable that is used to modulate the covariance function over the training data. We show how our approach can be used to model multi-modal and non-stationary processes. We exemplify the approach on a set of synthetic data and provide results on real data from motion capture and geostatistics.