280 citations · 2k across the 81 of their papers we have counts for
9 papers · 1 filter
Stochastic Expectation Propagation for Large Scale Gaussian Process Classification
Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li +2
A method for large scale Gaussian process classification has been recently proposed based on expectation propagation (EP). Such a method allows Gaussian process classifiers to be t…
Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation
Thang D. Bui, José Miguel Hernández-Lobato, Yingzhen Li +2
Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wid…
Black-box -divergence Minimization
José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland +3
Black-box alpha (BB-) is a new approximate inference method based on the minimization of -divergences. BB- scales to large datasets because it can be implemented using sto…
Denoising without access to clean data using a partitioned autoencoder
Dan Stowell, Richard E. Turner
Training a denoising autoencoder neural network requires access to truly clean data, a requirement which is often impractical. To remedy this, we introduce a method to train an aut…
Invariant Models for Causal Transfer Learning
Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner +1
Methods of transfer learning try to combine knowledge from several related tasks (or domains) to improve performance on a test task. Inspired by causal methodology, we relax the us…
Stochastic Expectation Propagation
Yingzhen Li, Jose Miguel Hernandez-Lobato, Richard E. Turner
Expectation propagation (EP) is a deterministic approximation algorithm that is often used to perform approximate Bayesian parameter learning. EP approximates the full intractable…
Neural Adaptive Sequential Monte Carlo
Shixiang Gu, Zoubin Ghahramani, Richard E. Turner
Sequential Monte Carlo (SMC), or particle filtering, is a popular class of methods for sampling from an intractable target distribution using a sequence of simpler intermediate dis…
On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes
Alexander G. de G. Matthews, James Hensman, Richard E. Turner +1
The variational framework for learning inducing variables (Titsias, 2009a) has had a large impact on the Gaussian process literature. The framework may be interpreted as minimizing…
Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs
Yarin Gal, Richard Turner
Standard sparse pseudo-input approximations to the Gaussian process (GP) cannot handle complex functions well. Sparse spectrum alternatives attempt to answer this but are known to…