# Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes

arXiv: https://arxiv.org/abs/2202.03287 · HTML: https://mlsanity.com/paper/2202.03287/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 7 Feb 2022.

## Builds on (most cited first, 11 of 11)

- [Gaussian Processes for Data-Efficient Learning in Robotics and Control](https://mlsanity.com/paper/1502.02860/) (2015)
- [Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions](https://mlsanity.com/paper/1410.7827/) (2014)
- [Structure Learning in Graphical Modeling](https://mlsanity.com/paper/1606.02359/) (2016)
- [Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression](https://mlsanity.com/paper/1806.00720/) (2018)
- [Ranking and combining multiple predictors without labeled data](https://mlsanity.com/paper/1303.3257/) (2013)
- [Unsupervised Ensemble Learning with Dependent Classifiers](https://mlsanity.com/paper/1510.05830/) (2015)
- [Nested Kriging predictions for datasets with large number of observations](https://mlsanity.com/paper/1607.05432/) (2016)
- [Nonparametric and high-dimensional functional graphical models](https://mlsanity.com/paper/2103.10568/) (2021)
- [Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimizations](https://mlsanity.com/paper/1702.08651/) (2017)
- [Bayesian Inference in Nonparanormal Graphical Models](https://mlsanity.com/paper/1806.04334/) (2018)
- [Aggregating Dependent Gaussian Experts in Local Approximation](https://mlsanity.com/paper/2010.08873/) (2020)

## Abstract (arXiv)

Distributed Gaussian process (DGP) is a popular approach to scale GP to big data which divides the training data into some subsets, performs local inference for each partition, and aggregates the results to acquire global prediction. To combine the local predictions, the conditional independence assumption is used which basically means there is a perfect diversity between the subsets. Although it keeps the aggregation tractable, it is often violated in practice and generally yields poor results. In this paper, we propose a novel approach for aggregating the Gaussian experts' predictions by Gaussian graphical model (GGM) where the target aggregation is defined as an unobserved latent variable and the local predictions are the observed variables. We first estimate the joint distribution of latent and observed variables using the Expectation-Maximization (EM) algorithm. The interaction between experts can be encoded by the precision matrix of the joint distribution and the aggregated predictions are obtained based on the property of conditional Gaussian distribution. Using both synthetic and real datasets, our experimental evaluations illustrate that our new method outperforms other state-of-the-art DGP approaches.