Publications

We develop metrics for measuring the quality of synthetic health data for both education and research. We use novel and existing …

Learning new representations of data to reduce correlation with sensitive attributes is one method to tackle algorithmic bias. In this …

This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, …

In this report we present our results on reproducing the paper titled Making AI Forget You: Data Deletion in Machine Learning by Ginart …

Synthetic medical data which preserves privacy while maintaining utility can be used as an alternative to real medical data, which has …

This paper builds on the results of the ESANN 2019 conference paper Privacy Preserving Synthetic Health Data [16], which develops …

We examine the feasibility of using synthetic medical data generated by GANs in the classroom, to teach data science in health …

Posts

There have been many advances in the field of Machine Learning in the recent years, but considering how data-intensive this field is …

Natural Language Generation (NLG) is characterised as “the sub-field of artificial intelligence and computational linguistics …