Speaker
Dr
Péter Antal
(Budapest University of Technology and Economy)
Description
Probabilistic graphical models are successfully applied in many challenging problems of artificial intelligence and machine learning: in data and knowledge fusion, in causal inference, in trustworthy decision support systems or explanation generation. First, I summarize that their wide applicability stems from their transparent, multifaceted semantics. Second, I show that the same property makes them an ideal representation for federated and privacy-preserving extensions in these areas. I demonstrate the applicability of probabilistic graphical models in exploring dependency models in large-scale health datasets.
Primary author
Dr
Péter Antal
(Budapest University of Technology and Economy)
