Autonomous Learning in a Dynamic World

This project will develop new machine learning algorithms that adapt autonomously to change. This capacity is critical, as change is constant and rare are the situations in which the system we model remains static. In contrast, most of machine learning research focuses on learning static models. Such models inevitably fail to be relevant to current circumstances. This project will develop new techniques that can adapt autonomously and dynamically to different types and rates of drift in different parts of the distribution from which the stream of data is sampled. They will deliver autonomous capacity for continuous learning in a dynamic world.