Feature Learning for Bayesian Inference
At a glance
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).
Ulzega, Simone; Albert, Carlo; Beer, Jürg,
5th Swiss SCOSTEP Workshop, Windisch, Switzerland, 15-16 May 2023.
Bacci, Marco; Sukys, Jonas; Reichert, Peter; Ulzega, Simone; Albert, Carlo,
Stochastic Environmental Research and Risk Assessment.
Available from: https://doi.org/10.1007/s00477-023-02434-z
Ulzega, Simone; Albert, Carlo,
3rd biennial meeting of the ISBA Section on Bayesian Computation (Bayes Comp), Levi, Finnland, 15-17 March 2023.