Bayesian experimental design (BED) provides a powerful and general framework for
optimizing the design of experiments. However, its deployment often poses substantial …

Many modern unsupervised or semi-supervised machine learning algorithms rely on
Bayesian probabilistic models. These models are usually intractable and thus require …

Abstract Estimating and optimizing Mutual Information (MI) is core to many problems in
machine learning, but bounding MI in high dimensions is challenging. To establish tractable …

Despite intense interest in expanding chemical space, libraries containing hundreds-of-
millions to billions of diverse molecules have remained inaccessible. Here we investigate …

We propose a new regularization method based on virtual adversarial loss: a new measure
of local smoothness of the conditional label distribution given input. Virtual adversarial loss …

We develop a generalisation of disentanglement in variational autoencoders (VAEs)—
decomposition of the latent representation—characterising it as the fulfilment of two factors …

Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …

Tackling the most pressing problems for humanity, such as the climate crisis and the threat
of global pandemics, requires accelerating the pace of scientific discovery. While science …

This book is a graduate-level introduction to probabilistic programming. It not only provides a
thorough background for anyone wishing to use a probabilistic programming system, but …

Abstract We introduce Deep Adaptive Design (DAD), a method for amortizing the cost of
adaptive Bayesian experimental design that allows experiments to be run in real-time …