65 citations · 247 across the 13 of their papers we have counts for
6 papers · 1 filter
Non-Factorised Variational Inference in Dynamical Systems
Alessandro Davide Ialongo, Mark van der Wilk, James Hensman +1
We focus on variational inference in dynamical systems where the discrete time transition function (or evolution rule) is modelled by a Gaussian process. The dominant approach so f…
Infinite-Horizon Gaussian Processes
Arno Solin, James Hensman, Richard E. Turner
Gaussian processes provide a flexible framework for forecasting, removing noise, and interpreting long temporal datasets. State space modelling (Kalman filtering) enables these non…
Gaussian Process Conditional Density Estimation
Vincent Dutordoir, Hugh Salimbeni, Marc Deisenroth +1
Conditional Density Estimation (CDE) models deal with estimating conditional distributions. The conditions imposed on the distribution are the inputs of the model. CDE is a challen…
Learning Invariances using the Marginal Likelihood
Mark van der Wilk, Matthias Bauer, ST John +1
Generalising well in supervised learning tasks relies on correctly extrapolating the training data to a large region of the input space. One way to achieve this is to constrain the…
Large-Scale Cox Process Inference using Variational Fourier Features
S. T. John, James Hensman
Gaussian process modulated Poisson processes provide a flexible framework for modelling spatiotemporal point patterns. So far this had been restricted to one dimension, binning to…
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models
Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman
The natural gradient method has been used effectively in conjugate Gaussian process models, but the non-conjugate case has been largely unexplored. We examine how natural gradients…