Optimization & Learning

OptV1In this theme we look at optimization and learning in directed and undirected graphical models. undirected graphical models have factors that depend on many variables. This includes higher-order models as well as models with a large, even continuous, label space. Optimizing in such models is often NP-hard. Furthermore, it is often difficult to hand-craft the functional relationship between variables, hence it is necessary to learn them. The goal in this theme is to analyse the trade-offs between models, optimization and learning with the ultimate goal of achieving practically relevant algorithms which are efficient and accurate. A shortlist of research topics we are excited about: a) optimization in undirected graphical models with higher-order factors, continuous label space, and models of very large size; b) combining generative and discriminative models; c) probabilistic learning and inference in undirected graphical models; d) combining deep directed models with undirected graphical models.

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