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Feature Learning for Bayesian Inference

At a glance

  • Project leader : Prof. Dr. Antonietta Mira
  • Co-project leader : Prof. Dr. Fernando Perez-Cruz
  • Project team : Dr. Carlo Albert, Prof. Alessandro Laio, Prof. Jukka-Pekka Onnela, Dr. Simone Ulzega
  • Project status : ongoing
  • Funding partner : SNSF
  • Contact person : Simone Ulzega


The goal of this project is to use interpretable Machine Learning (ML) to find low-dimensional features in high-dimensional noisy data generated by (i) stochastic models or (ii) real systems. In both cases, the problem is to disentangle the effect of high-dimensional disturbances, such as noise or unobserved inputs, from the effects of relevant characteristics (model parameters in the first case, system properties in the latter).