Research of the Probabilistic Machine Learning Group

We develop new methods for probabilistic modeling, Bayesian inference and machine learning. Our current focuses are:

  • Agile Probabilistic AI
  • Simulator-based Inference
  • Bayesian Deep Learning
  • Privacy-preserving and Secure AI
  • Interactive AI
  • Applications Incl. Health

  • These topics match the research programs of FCAI which we contribute to.

    Agile Probabilistic AI

    We develop and improve a principled Bayesian data-analysis workflow. Our core research is on designing computational diagnostics, devising methods and putting together tools that assist domain-expert data analysts in an agile and iterative workflow. This is done by building modeling tools and methods independently of the actual implementation in domain-specific fields. This enables the research to cover a wide range of models, such as Bayesian hierarchical models and Gaussian processes. The research is disseminated as free and modular open-source software, including software for the most popular probabilistic programming framework Stan. Some of the projects we contribute towards include projpred (core contributions), loo (core contributions), ArviZ, and viabel.

    Representative Publications

    Simulator-based Inference

    Approximate Bayesian computation (ABC) and other likelihood-free methods have gained popularity across scientific fields, as they enable statistical inference with complex models that only exist as computer simulators. We focus on improving sample-efficiency, making likelihood-free inference (LFI) possible even on difficult problems where each single simulator run is computationally costly. In ongoing research, we harness this property to combine LFI and dynamical models. Important drivers for the theoretical work come from challenges we encounter in applying the methods in multiple fields, including cognitive science, population genetics, neuroscience, epidemiology, ecology and business-to-business marketing.

    We develop the Engine for Likelihood-free Inference (ELFI,, an open-source Python software library that is designed to be modular and extensible. It provides a wide range of ABC algorithms, supports convenient syntax for defining the inference task and allows working with complex model hierarchies.

    Representative Publications

    Bayesian Deep Learning

    We augment traditional deep learning methods with uncertainty from Bayesian principles, using Gaussian process and Bayesian neural network families, and extending to continuous-time dynamics. For Gaussian processes we develop novel deep function spaces and flexible covariance structures under model marginalisation. We develop scalable Bayesian neural networks by studying functionally diverse posteriors. We propose novel approaches to deep learning as continuous-time flows, replacing the conventional sequence of discrete mappings in deep neural networks.

    Representative Publications