Deep Gaussian Process

Deep Gaussian processes (DGPs) are hierarchical Bayesian models extending the capabilities of standard Gaussian processes to model complex, multi-layered relationships in data. Current research focuses on improving the efficiency and scalability of DGP inference, often employing variational inference techniques, stochastic differential equations, and sparse inducing point methods to approximate intractable posterior distributions. These advancements aim to enhance the accuracy and applicability of DGPs across diverse fields, including time series analysis, multifidelity modeling, and image processing, where they offer robust uncertainty quantification and the ability to handle non-linear relationships. The resulting models show promise for improving predictions and uncertainty estimates in various scientific and engineering applications.

Papers

October 31, 2024

Residual Deep Gaussian Processes on Manifolds
Kacper Wyrwal, Andreas Krause, Viacheslav Borovitskiy
Class Manifold Riemannian Manifold Residual Neural Network Deep Gaussian Process

October 29, 2024

Deep Q-Exponential Processes
Zhi Chang, Chukwudi Obite, Shuang Zhou, Shiwei Lan
Deep Q Network Deep Gaussian Process Shallow Layer

September 18, 2024

Amortized Variational Inference for Deep Gaussian Processes
Qiuxian Meng, Yongyou Zhang
Gaussian Process Variational Inference Deep Gaussian Process Amortized Variational Inference

August 12, 2024

Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations
Jian Xu, Zhiqi Lin, Min Chen, Junmei Yang, Delu Zeng, John Paisley
Gaussian Process Stochastic Differential Equation Posterior Approximation Deep Gaussian Process

July 24, 2024

Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference
Jian Xu, Delu Zeng, John Paisley
Diffusion Model Variational Inference Many Sparse Posterior Inference Exceptional Point Bayesian Deep Learning Deep Gaussian Process

July 1, 2024

Adaptive RKHS Fourier Features for Compositional Gaussian Process Models
Xinxing Shi, Thomas Baldwin-McDonald, Mauricio A. Álvarez
Gaussian Process Reproducing Kernel Hilbert Space Fourier Feature Compositional Data Deep Gaussian Process Stochastic Variational Inference

February 25, 2024

Gradient-enhanced deep Gaussian processes for multifidelity modelling
Viv Bone, Chris van der Heide, Kieran Mackle, Ingo H. J. Jahn, Peter M. Dower, Chris Manzie
Gaussian Process Gradient Information Deep Gaussian Process GP Regression Multifidelity Linear Regression

January 23, 2024

Towards Improved Variational Inference for Deep Bayesian Models
Sebastian W. Ober
Gaussian Process Variational Inference Posterior Inference Variational Posterior Deep Gaussian Process Deep Bayesian

November 24, 2023

Deep Latent Force Models: ODE-based Process Convolutions for Bayesian Deep Learning
Thomas Baldwin-McDonald, Mauricio A. Álvarez
Uncertainty Quantification Bayesian Deep Learning Deep Gaussian Process Stochastic Variational Inference Convolutional Neural Process Latent Force Model

October 27, 2023

Deep Transformed Gaussian Processes
Francisco Javier Sáez-Maldonado, Juan Maroñas, Daniel Hernández-Lobato
Variational Inference Deep Gaussian Process Inference Algorithm Transformed Gaussian Process

October 17, 2023

Thin and Deep Gaussian Processes
Daniel Augusto de Souza, Alexander Nikitin, ST John, Magnus Ross, Mauricio A. Álvarez, Marc Peter Deisenroth, João P. P. Gomes, Diego Mesquita, César Lincoln C. Mattos
Gaussian Process Hyper Parameter Dimensional Embeddings Deep Gaussian Process

September 22, 2023

Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes
Jian Xu, Shian Du, Junmei Yang, Qianli Ma, Delu Zeng
Gaussian Process Operator Learning Deep Gaussian Process Deep Nonparametric

September 18, 2023

Convolutional Deep Kernel Machines
Edward Milsom, Ben Anson, Laurence Aitchison
Deep Gaussian Process Deep Kernel Learning Neural Network Gaussian Process

June 2, 2023

Linked Deep Gaussian Process Emulation for Model Networks
Deyu Ming, Daniel Williamson
Gaussian Process Deep Gaussian Process Network Model

May 23, 2023

An Improved Variational Approximate Posterior for the Deep Wishart Process
Sebastian Ober, Ben Anson, Edward Milsom, Laurence Aitchison
Deep Gaussian Process Deep Bayesian

March 6, 2023

January 29, 2023

Sequential Estimation of Gaussian Process-based Deep State-Space Models
Yuhao Liu, Marzieh Ajirak, Petar Djuric
RSD Difference of Gaussian Deep State Space Model Deep Gaussian Process Sequential Estimation

November 18, 2022

Deep Gaussian Processes for Air Quality Inference
Aadesh Desai, Eshan Gujarathi, Saagar Parikh, Sachin Yadav, Zeel Patel, Nipun Batra
Fine Grained Air Quality Deep Gaussian Process Stochastic Variational Inference

October 18, 2022

Locally Smoothed Gaussian Process Regression
Davit Gogolashvili, Bogdan Kozyrskiy, Maurizio Filippone
Gaussian Process Regression Deep Gaussian Process