Multi-Task Learning: Optimisation Methods and Applications

This project is focused on multi-task learning (MTL) for the purposes of developing optimisation methods, statistical analysis and applications. On the theoretical side, we propose to develop a new generation of MTL algorithms; on the practical side, we will explore applications of these algorithms in the areas of marketing science, bioinformatics and robot learning. As an increasing number of data analysis problems require learning from multiple data sources, MTL should receive more attention in Machine Learning and we expect that more researchers will work on this topic in the coming years. We are particularly interested in optimisation approaches to MTL. In particular, our proposed approach will: 1) allow one to model constraints among the tasks; 2) allow semi-supervised learning — only some of the tasks have available data but we still wish to learn all tasks; 3) lead to efficient optimisation algorithms; 4) subsume related frameworks such as collaborative filtering and learning vector fields.