Abstract

Machine learning studies the problem of learning to perform a given task from a dataset of examples. A fundamental limitation of standard machine learning methods is the cost incurred in preparing large training datasets. Often in applications a limited number of examples is available and the task cannot be solved in isolation. A potential remedy is offered by multitask learning, which aims to learn several related tasks simultaneously. If the tasks share some constraining or generative property which is sufficiently simple it should allow for better learning of the individual tasks even when the individual training datasets are small. In the talk, I will present a wide class of multitask learning methods which encourage different forms of task relatedness and involve certain notions of structured sparsity and low rank tensor representations. I will also discuss iterative algorithms to implement these methods, building upon ideas from convex optimisation. Finally, I will illustrate the performance of these methods in applications arising in affective computing, computer vision and user modelling.