The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …

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

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 …

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 …

Good sparse approximations are essential for practical inference in Gaussian Processes as
the computational cost of exact methods is prohibitive for large datasets. The Fully …

Abstract Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of
Gaussian processes (GPs) and are formally equivalent to neural networks with multiple …

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 …

Deep neural networks (DNNs) have excellent representative power and are state of the art
classifiers on many tasks. However, they often do not capture their own uncertainties well …

This paper proposes a novel scheme for reduced-rank Gaussian process regression. The
method is based on an approximate series expansion of the covariance function in terms of …

We present a practical way of introducing convolutional structure into Gaussian processes,
making them more suited to high-dimensional inputs like images. The main contribution of …