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).
Bacci, Marco; Sukys, Jonas; Reichert, Peter; Ulzega, Simone; Albert, Carlo,
A comparison of numerical approaches for statistical inference with stochastic models.
Stochastic Environmental Research and Risk Assessment.
Available from: https://doi.org/10.1007/s00477-023-02434-z
Ulzega, Simone; Albert, Carlo,
Boosting Bayesian parameter inference of SDE models by Hamiltonian scale separation : a real-world case study in urban hydrology.
3rd biennial meeting of the ISBA Section on Bayesian Computation (Bayes Comp), Levi, Finnland, 15-17 March 2023.