Deep Gaussian Processes with Decoupled Inducing Inputs

01/09/2018
by   Marton Havasi, et al.
0

Deep Gaussian Processes (DGP) are hierarchical generalizations of Gaussian Processes (GP) that have proven to work effectively on a multiple supervised regression tasks. They combine the well calibrated uncertainty estimates of GPs with the great flexibility of multilayer models. In DGPs, given the inputs, the outputs of the layers are Gaussian distributions parameterized by their means and covariances. These layers are realized as Sparse GPs where the training data is approximated using a small set of pseudo points. In this work, we show that the computational cost of DGPs can be reduced with no loss in performance by using a separate, smaller set of pseudo points when calculating the layerwise variance while using a larger set of pseudo points when calculating the layerwise mean. This enabled us to train larger models that have lower cost and better predictive performance.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/21/2021

Adaptive Inducing Points Selection For Gaussian Processes

Gaussian Processes (GPs) are flexible non-parametric models with strong ...
research
10/14/2022

Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes

The quality of many modern machine learning models improves as model com...
research
06/27/2012

Variable noise and dimensionality reduction for sparse Gaussian processes

The sparse pseudo-input Gaussian process (SPGP) is a new approximation m...
research
05/26/2018

Calibrating Deep Convolutional Gaussian Processes

The wide adoption of Convolutional Neural Networks (CNNs) in application...
research
06/27/2022

Distributional Gaussian Processes Layers for Out-of-Distribution Detection

Machine learning models deployed on medical imaging tasks must be equipp...
research
11/14/2019

Scalable Exact Inference in Multi-Output Gaussian Processes

Multi-output Gaussian processes (MOGPs) leverage the flexibility and int...
research
10/28/2020

Hierarchical Gaussian Processes with Wasserstein-2 Kernels

We investigate the usefulness of Wasserstein-2 kernels in the context of...

Please sign up or login with your details

Forgot password? Click here to reset