Combinatorial Optimization


In 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:

  1. optimization in undirected graphical models with higher-order factors, continuous label space, and models of very large size;
  2. combining generative and discriminative models;
  3. probabilistic learning and inference in undirected graphical models;
  4. combining deep directed models with undirected graphical models.

Running Projects:

Recent (selected) publications and preprints:

Text-book: B. Savchynskyy Discrete Graphical Models – An Optimization Perspective  Under review.