Gaussian Processes for Big Data (2013-08-11T00:00:00.000000Z)

TL;DR

Stochastic variational inference for Gaussian process models is introduced and it is shown how GPs can be variationally decomposed to depend on a set of globally relevant inducing variables which factorize the model in the necessary manner to perform Variational inference.

Abstract

We introduce stochastic variational inference for Gaussian process models. This enables the application of Gaussian process (GP) models to data sets containing millions of data points. We show how GPs can be variationally decomposed to depend on a set of globally relevant inducing variables which factorize the model in the necessary manner to perform variational inference. Our approach is readily extended to models with non-Gaussian likelihoods and latent variable models based around Gaussian processes. We demonstrate the approach on a simple toy problem and two real world data sets.

Authors

Neil D. Lawrence

4 papers

J. Hensman

1 papers

Nicoló Fusi

1 papers

Gaussian Processes for Big Data

TL;DR

Abstract

Authors

References17 items

Stochastic variational inference

Fast Variational Inference in the Conjugate Exponential Family

Manifold Relevance Determination

Variational Gaussian Process Dynamical Systems

Computationally Efficient Convolved Multiple Output Gaussian Processes

Bayesian Gaussian Process Latent Variable Model

Variational Learning of Inducing Variables in Sparse Gaussian Processes

Sparse probabilistic regression for activity-independent human pose inference

Local and global sparse Gaussian process approximations

A Unifying View of Sparse Approximate Gaussian Process Regression

Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models

Assessing Approximate Inference for Binary Gaussian Process Classification

Sparse On-Line Gaussian Processes

Variational Gaussian process classifiers

Fast Forward Selection to Speed Up Sparse Gaussian Process Regression

We note several interesting results

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