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Neural Diffusion Processes
Vincent Dutordoir, Alan Saul, Zoubin Ghahramani +1
Neural network approaches for meta-learning distributions over functions have desirable properties such as increased flexibility and a reduced complexity of inference. Building on…
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 Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain
Fergus Simpson, Alexis Boukouvalas, Vaclav Cadek +2
In the univariate setting, using the kernel spectral representation is an appealing approach for generating stationary covariance functions. However, performing the same task for m…
Marginalised Gaussian Processes with Nested Sampling
Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen
Gaussian Process (GPs) models are a rich distribution over functions with inductive biases controlled by a kernel function. Learning occurs through the optimisation of kernel hyper…