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.

    Representative Publications

    Simulator-based Inference

    Figure: Illustration of using multiple runs of a stochastic simulator M to approximate the likelihood. (From Lintusaari et al., 2016)

    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 possible even on difficult problems where each single simulator run is computationally costly. Important drivers for the theoretical work come from challenges we encounter in applying the methods in multiple fields, including cognitive science, population genetics, epidemiology, ecology and business-to-business marketing. We develop the Engine for Likelihood-free Inference (ELFI, http://elfi.ai), an open-source Python software library that is designed to be modular and extensible.

    Representative Publications