55 citations · 116 across the 3 of their papers we have counts for
6 papers
Convolutional Gaussian Processes
Mark van der Wilk, Carl Edward Rasmussen, James Hensman
We present a practical way of introducing convolutional structure into Gaussian processes, making them more suited to high-dimensional inputs like images. The main contribution of…
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction
Hossein Soleimani, James Hensman, Suchi Saria
Missing data and noisy observations pose significant challenges for reliably predicting events from irregularly sampled multivariate time series (longitudinal) data. Imputation met…
Identification of Gaussian Process State Space Models
Stefanos Eleftheriadis, Thomas F. W. Nicholson, Marc Peter Deisenroth +1
The Gaussian process state space model (GPSSM) is a non-linear dynamical system, where unknown transition and/or measurement mappings are described by GPs. Most research in GPSSMs…
Chained Gaussian Processes
Alan D. Saul, James Hensman, Aki Vehtari +1
Gaussian process models are flexible, Bayesian non-parametric approaches to regression. Properties of multivariate Gaussians mean that they can be combined linearly in the manner o…
MCMC for Variationally Sparse Gaussian Processes
James Hensman, Alexander G. de G. Matthews, Maurizio Filippone +1
Gaussian process (GP) models form a core part of probabilistic machine learning. Considerable research effort has been made into attacking three issues with GP models: how to compu…
Spike and Slab Gaussian Process Latent Variable Models
Zhenwen Dai, James Hensman, Neil Lawrence
The Gaussian process latent variable model (GP-LVM) is a popular approach to non-linear probabilistic dimensionality reduction. One design choice for the model is the number of lat…