Multi-resolution Multi-task Gaussian Processes: London air pollution
Ollie Hamelijnck · The Alan Turing Institute, London
Thu, Jul 9, 2020 · 13:00 UTC
Poor air quality in cities is a significant threat to health and life expectancy, with over 80% of people living in urban areas exposed to air quality levels that exceed World Health Organisation limits. In this session, I present a multi-resolution multi-task framework that handles evidence integration under varying spatio-temporal sampling resolution and noise levels. We have developed both shallow Gaussian Process (GP) mixture models and deep GP constructions that naturally handle this evidence integration, as well as biases in the mean. These models underpin our work at the Alan Turing In