Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Gaussian processes are the gold standard for many real-world modeling problems,
especially in cases where a model's success hinges upon its ability to faithfully represent …

We introduce a new class of inter-domain variational Gaussian processes (GP) where data
is mapped onto the unit hypersphere in order to use spherical harmonic representations …

As Gaussian processes are used to answer increasingly complex questions, analytic
solutions become scarcer and scarcer. Monte Carlo methods act as a convenient bridge for …

We consider the optimal approximate posterior over the top-layer weights in a Bayesian
neural network for regression, and show that it exhibits strong dependencies on the lower …

With a principled representation of uncertainty and closed form posterior updates, Gaussian
processes (GPs) are a natural choice for online decision making. However, Gaussian …

Variational inference techniques based on inducing variables provide an elegant framework
for scalable posterior estimation in Gaussian process (GP) models. Besides enabling …

Gaussian processes (GPs) are flexible priors for modeling functions. However, their success
depends on the kernel accurately reflecting the properties of the data. One of the appeals of …

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 …

Inverse problems involving partial differential equations (PDEs) are widely used in science
and engineering. Although such problems are generally ill-posed, different regularisation …