Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

Synthetic data consists of artificially generated data. When data are scarce, or of poor
quality, synthetic data can be used, for example, to improve the performance of machine …

This explainer document aims to provide an overview of the current state of the rapidly
expanding work on synthetic data technologies, with a particular focus on privacy. The …

Artificial intelligence (AI) has been heralded as one of the key technological innovations of
the 21st century. Within healthcare, much attention has been placed upon the ability of …

Generative adversarial networks (GANs) have been extremely successful in generating
samples, from seemingly high dimensional probability measures. However, these methods …

Evaluating the performance of machine learning models on diverse and underrepresented
subgroups is essential for ensuring fairness and reliability in real-world applications …

Data augmentation is an important procedure in deep learning. GAN-based data
augmentation can be utilized in many domains. For instance, in the credit card fraud domain …

Tabular datasets are ubiquitous in data science applications. Given their importance, it
seems natural to apply state-of-the-art deep learning algorithms in order to fully unlock their …

Modelling in finance is a challenging task: the data often has complex statistical properties
and its inner workings are largely unknown. Deep learning algorithms are making progress …

We investigate the use of Generative Adversarial Networks (GANs) for probabilistic
forecasting of financial time series. To this end, we introduce a novel economics-driven loss …