Deep kernel learning combines the non-parametric flexibility of kernel methods with the
inductive biases of deep learning architectures. We propose a novel deep kernel learning …

This work brings together two powerful concepts in Gaussian processes: the variational
approach to sparse approximation and the spectral representation of Gaussian processes …

We introduce a framework for Continual Learning (CL) based on Bayesian inference over
the function space rather than the parameters of a deep neural network. This method …

Gaussian processes (GPs) are flexible distributions over functions that enable highlevel
assumptions about unknown functions to be encoded in a parsimonious, flexible and …

We present a novel extension of multi-output Gaussian processes for handling
heterogeneous outputs. We assume that each output has its own likelihood function and use …

Sparse pseudo-point approximations for Gaussian process (GP) models provide a suite of
methods that support deployment of GPs in the large data regime and enable analytic …

Abstract Gaussian Processes (GPs) models have been successfully applied to the problem
of learning from sequential observations. In such context, the family of Recurrent Gaussian …

Gaussian Processes (GP) can be used as flexible, non-parametric function priors. Inspired
by the growing body of work on Normalizing Flows, we enlarge this class of priors through a …

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 …

Neural processes (NPs) constitute a family of variational approximate models for stochastic
processes with promising properties in computational efficiency and uncertainty …