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Gaussian Processes for Big Data
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 howExpand
Scalable Variational Gaussian Process Classification
We show how to scale the model within a variational inducing point framework, outperforming the state of the art on benchmark datasets. Expand
GPflow: A Gaussian Process Library using TensorFlow
GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end. Expand
MCMC for Variationally Sparse Gaussian Processes
We present a general inference scheme for general GP models, using a variational approximation to the posterior which is sparse in support of the function but otherwise free-form. Expand
Variational Fourier Features for Gaussian Processes
This work brings together two powerful concepts in Gaussian processes: the variational approach to sparse approximation and the spectral representation of Gaussian process. Expand
Convolutional Gaussian Processes
We present a practical way of introducing convolutional structure into Gaussian processes, making them more suited to high-dimensional inputs like images. Expand
On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes
We give a new proof of the result for infinite index sets which allows inducing points that are not data points and likelihoods that depend on all function values. Expand
Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters
We propose hierarchical Gaussian processes as a general model of gene expression time-series, with application to a variety of problems such as data fusion and clustering. Expand
Chained Gaussian Processes
We develop an approximate inference procedure for Chained Gaussian Processes that is scalable and applicable to any factorized likelihood. Expand
Hoxa2 Selectively Enhances Meis Binding to Change a Branchial Arch Ground State
Summary Hox transcription factors (TFs) are essential for vertebrate development, but how these evolutionary conserved proteins function in vivo remains unclear. Because Hox proteins have notoriouslyExpand