Quadruply Stochastic Gaussian Processes

06/04/2020
by   Trefor W. Evans, et al.
0

We introduce a stochastic variational inference procedure for training scalable Gaussian process (GP) models whose per-iteration complexity is independent of both the number of training points, n, and the number basis functions used in the kernel approximation, m. Our central contributions include an unbiased stochastic estimator of the evidence lower bound (ELBO) for a Gaussian likelihood, as well as a stochastic estimator that lower bounds the ELBO for several other likelihoods such as Laplace and logistic. Independence of the stochastic optimization update complexity on n and m enables inference on huge datasets using large capacity GP models. We demonstrate accurate inference on large classification and regression datasets using GPs and relevance vector machines with up to m = 10^7 basis functions.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/23/2019

Sparse Orthogonal Variational Inference for Gaussian Processes

We introduce a new interpretation of sparse variational approximations f...
research
02/25/2018

Conditionally Independent Multiresolution Gaussian Processes

We propose a multiresolution Gaussian process (GP) model which assumes c...
research
11/18/2016

Faster variational inducing input Gaussian process classification

Gaussian processes (GP) provide a prior over functions and allow finding...
research
09/02/2016

Generic Inference in Latent Gaussian Process Models

We develop an automated variational method for inference in models with ...
research
05/27/2019

Scalable Training of Inference Networks for Gaussian-Process Models

Inference in Gaussian process (GP) models is computationally challenging...
research
06/23/2020

Variational Orthogonal Features

Sparse stochastic variational inference allows Gaussian process models t...
research
09/08/2018

Non-Parametric Variational Inference with Graph Convolutional Networks for Gaussian Processes

Inference for GP models with non-Gaussian noises is computationally expe...

Please sign up or login with your details

Forgot password? Click here to reset