Alex Jung, Aalto University, Finland


Information-Theoretic Limits of Learning Networks for Big Data


One of the most powerful approaches to master big data applicatinos is to organize the data as networks. However, in many applications the underlying network structure has to be learnt from training data. In this talk i will discuss some fundamental limits for learning such networks using an information-theoretic view on probabilistic graphical models.