Combinatorial Optimization

OptV1

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) articles: