We propose a lower bound on the log marginal likelihood of Gaussian process regression models that can be computed without matrix factorisation of the full kernel matrix. We show that approximate maximum likelihood learning of model parameters by …
In decision-making systems, it is important to have classifiers that have calibrated uncertainties, with an optimisation objective that can be used for automated model selection and training. Gaussian processes (GPs) provide uncertainty estimates and …
In this work, we provide a variational lower bound that can be computed without expensive matrix operations like inversion. Our bound can be used as a drop-in replacement to the existing variational method of Hensman et al. (2013, 2015), and can …
We identify a new variational inference scheme for dynamical systems whose transition function is modelled by a Gaussian process. Inference in this setting has either employed computationally intensive MCMC methods, or relied on factorisations of the …
Excellent variational approximations to Gaussian process posteriors have been developed which avoid the $mathcalOłeft(N^3i̊ght)$ scaling with dataset size $N$. They reduce the computational cost to $mathcalOłeft(NM^2g̊ht)$, with $Młl N$ the number of …