The kernel function and its hyperparameters are the central model select...
As Gaussian processes mature, they are increasingly being deployed as pa...
The Chernoff bound is a well-known tool for obtaining a high probability...
Bayesian neural networks (BNNs) combine the expressive power of deep lea...
Recent work in scalable approximate Gaussian process regression has disc...
In this paper, we investigate the question: Given a small number of
data...
We propose a lower bound on the log marginal likelihood of Gaussian proc...
Recent work has attempted to directly approximate the `function-space' o...
Gaussian processes are distributions over functions that are versatile a...
Sparse stochastic variational inference allows Gaussian process models t...
We consider the problem of optimising functions in the Reproducing kerne...
Neural networks provide state-of-the-art performance on a variety of tas...
Excellent variational approximations to Gaussian process posteriors have...