Open Topics

More on the formal side

In the context of probabilistic inference

  • Scalability of the colouring algorithm for lifting models and adaptation of noisy symmetries (BA / MA)
  • Applying lifting to tensor networks (BA / MA)
  • Sampling in the (lifted) junction tree algorithm
  • Lifting of variable eliminination in the Fourier domain (MA)
  • Lifting importance sampling in parfactor graphs (MA)

In the context of decision making

In the context of privacy

  • Analysis of spectral clustering for differential privacy (more MA level)

More application-oriented

In the context of databases

  • Oblivious Databases (BA/MA; specific focus depends on personal interests; can range from a literature study or a formal analysis to implementation; in collaboration with Thore Thießen, RG Efficient Algorithms)

In the context of text modelling

  • Learning relational models using RDF triples extracted from text (BA / MA)

At the intersection of ML and PGMs

  • Training of a neural network for probabilistic inference (BA)

In collaboration with the Institute of Medical Informatics (Tobias Brix)