Aggregated data is commonplace in areas such as epidemiology and demography. For example, census data for a population is usually given as averages defined over time periods or spatial resolutions (cities, regions or countries). In this paper, we present a novel multi-task learning model based on Gaussian processes for joint learning of variables that have been aggregated at different input scales. Our model represents each task as the linear combination of the realizations of latent processes that are integrated at a different scale per task. We are then able to compute the cross-covariance between the different tasks either analytically or numerically. We also allow each task to have a potentially different likelihood model and provide a variational lower bound that can be optimised in a stochastic fashion making our model suitable for larger datasets. We show examples of the model in a synthetic example, a fertility dataset, and an air pollution prediction application.
Her research interests are Gaussian Processes, data scarcity, imbalanced data and multi-task learning. She enjoys working on healthcare applications.
Fariba’s experience in chairing and organizing scientific events include: the Gaussian processes summer school and the Women in Machine Learning, where she was Programme Chair at the affinity workshop for ICML 2020.