Neural Processes (NPs)(Garnelo et al 2018a; b) approach regression by learning to map a
context set of observed input-output pairs to a distribution over regression functions. Each …

Learning from small data sets is critical in many practical applications where data collection
is time consuming or expensive, eg, robotics, animal experiments or drug design. Meta …

Bayesian optimization is a sample-efficient black-box optimization procedure that is typically
applied to a small number of independent objectives. However, in practice we often wish to …

Abstract Conditional Density Estimation (CDE) models deal with estimating conditional
distributions. The conditions imposed on the distribution are the inputs of the model. CDE is …

Abstract Deep Gaussian processes (DGPs) can model complex marginal densities as well
as complex mappings. Non-Gaussian marginals are essential for modelling real-world data …

Despite the recent progress in hyperparameter optimization (HPO), available benchmarks
that resemble real-world scenarios consist of a few and very large problem instances that …

Multi-output regression models must exploit dependencies between outputs to maximise
predictive performance. The application of Gaussian processes (GPs) to this setting typically …

A broad class of stochastic volatility models are defined by systems of stochastic differential
equations, and while these models have seen widespread success in domains such as …

In transfer learning, we aim to improve the predictive modeling of a target output by using the
knowledge from some related source outputs. In real-world applications, the data from the …

Abstract Multi-output Gaussian processes (MOGPs) can help to improve predictive
performance for some output variables, by leveraging the correlation with other output …