One important problem in machine learning is how to extract knowledge from prior experience, then transfer and apply this knowledge in new learning tasks. To address this problem, ...
Abstract Clustering Stability methods are a family of widely used model selection techniques for data clustering. Their unifying theme is that an appropriate model should result in...
We use graphical models and structure learning to explore how people learn policies in sequential decision making tasks. Studies of sequential decision-making in humans frequently...
We consider reinforcement learning in systems with unknown dynamics. Algorithms such as E3 (Kearns and Singh, 2002) learn near-optimal policies by using "exploration policies...
Abstract. The Unifying Theories of Programming underpins the development of Circus, a state-rich process algebra for refinement. We have previously presented a theory of testing fo...