Fast, Locally Adaptive Inference for Machine Learning in Graphical Models

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Graphical models are a powerful tool in machine learning with successful applications in diverse areas such as medical diagnosis, natural language processing, robotics, speech recognition and analysis of genetic data. Despite this success, modern data sets place new demands on the graphical modelling framework, because the models can be enormous, but exact inference in graphical models is intractable. Despite the extensive literature on approximate inference, there is still a huge gap between the largest data sets that we wish to analyse and the largest graphical models that we can handle.In order to meet the challenges of these new applications, this project concerns new approximate inference algorithms for the large-scale graphical models that arise in practical applications of machine learning. Very few existing inference algorithms can handle extremely large models with continuous variables, and important classes of inference algorithms, such as Monte Carlo techniques, have not been scaled to such models at all. Computationally efficient inference would significantly expand the range of applications to which the graphical modelling framework can be applied.