# AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models

arXiv: https://arxiv.org/abs/1610.05392 · HTML: https://mlsanity.com/paper/1610.05392/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2016.

## Built on by (most cited first, 6 of 6)

- [Spatio-Temporal Variational Gaussian Processes](https://mlsanity.com/paper/2111.01732/) (2021)
- [Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations](https://mlsanity.com/paper/2408.06069/) (2024)
- [Gaussian Process-Gated Hierarchical Mixtures of Experts](https://mlsanity.com/paper/2302.04947/) (2023)
- [Bayesian optimization of distributed neurodynamical controller models for spatial navigation](https://mlsanity.com/paper/2111.00599/) (2021)
- [Locally Smoothed Gaussian Process Regression](https://mlsanity.com/paper/2210.09998/) (2022)
- [Identification of stormwater control strategies and their associated uncertainties using Bayesian Optimization](https://mlsanity.com/paper/2305.18630/) (2023)

## Abstract (arXiv)

We investigate the capabilities and limitations of Gaussian process models by jointly exploring three complementary directions: (i) scalable and statistically efficient inference; (ii) flexible kernels; and (iii) objective functions for hyperparameter learning alternative to the marginal likelihood. Our approach outperforms all previously reported GP methods on the standard MNIST dataset; performs comparatively to previous kernel-based methods using the RECTANGLES-IMAGE dataset; and breaks the 1% error-rate barrier in GP models using the MNIST8M dataset, showing along the way the scalability of our method at unprecedented scale for GP models (8 million observations) in classification problems. Overall, our approach represents a significant breakthrough in kernel methods and GP models, bridging the gap between deep learning approaches and kernel machines.