667 citations · 1.5k across the 26 of their papers we have counts for
33 papers
Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes
Talay M Cheema, Carl Edward Rasmussen
Sparse variational approximations are popular methods for scaling up inference and learning in Gaussian processes to larger datasets. For training points, exact inference has $…
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt +1
The kernel function and its hyperparameters are the central model selection choice in a Gaussian proces (Rasmussen and Williams, 2006). Typically, the hyperparameters of the kernel…
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
Alexander Terenin, David R. Burt, Artem Artemev +4
Gaussian processes are frequently deployed as part of larger machine learning and decision-making systems, for instance in geospatial modeling, Bayesian optimization, or in latent…
Kernel Identification Through Transformers
Fergus Simpson, Ian Davies, Vidhi Lalchand +3
Kernel selection plays a central role in determining the performance of Gaussian Process (GP) models, as the chosen kernel determines both the inductive biases and prior support of…
The Promises and Pitfalls of Deep Kernel Learning
Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk
Deep kernel learning (DKL) and related techniques aim to combine the representational power of neural networks with the reliable uncertainty estimates of Gaussian processes. One cr…
Ensembling geophysical models with Bayesian Neural Networks
Ushnish Sengupta, Matt Amos, J. Scott Hosking +3
Ensembles of geophysical models improve projection accuracy and express uncertainties. We develop a novel data-driven ensembling strategy for combining geophysical models using Bay…