Information theoretic methods for learning generative models for relational structures

This talk focusses on work aimed at developing a principled probabilistic and information theoretic framework for learning generative models of relational structure. The aim is develop methods that can be used to learn models that can capture the variability present in graph-structures used to represent shapes or arrangements of shape-primitives in images. Here nodes represent the parts of shape-primitives representing an object, and the the edges represent the relationships which prevail between the parts. The aim is to learn the relationships from examples. Of course such structures can exhibit variability in the arrangement of parts, and the data used in training can be subject to uncertainty. It hence represents a demanding learning problem, for which there is limited available methodology.