Automatic Chemical Design is a framework for generating novel molecules with optimized
properties. The original scheme, featuring Bayesian optimization over the latent space of a …

Some optimization problems are characterized by an objective that is very expensive, that
lacks an analytical expression, and whose evaluations can be contaminated by noise …

Many problems in machine learning and statistics involve nested expectations and thus do
not permit conventional Monte Carlo (MC) estimation. For such problems, one must nest …

Imagine a world where computational simulations can be inverted as easily as running them
forwards, where data can be used to refine models automatically, and where the only …

We propose practical extensions to Bayesian optimization for solving dynamic problems. We
model dynamic objective functions using spatiotemporal Gaussian process priors which …

Abstract Gaussian Processes (GPs) provide an extremely powerful mechanism to model a
variety of problems but incur an O (N 3) complexity in the number of data samples. Common …

We present variational inference with sequential sample-average approximation (VISA), a
method for approximate inference in computationally intensive models, such as those based …

We formalize the notion of nesting probabilistic programming queries and investigate the
resulting statistical implications. We demonstrate that while query nesting allows the …

Delayed and risky choice (DARC) experiments are a cornerstone of research in psychology,
behavioural economics and neuroeconomics. By collecting an agent's preferences between …

We present an extensive study of methods for exactly solving stochastic constraint
(optimisation) problems (SCPs) in network analysis. These problems are prevalent in …