Funded FWF stand alone project

The FWF funds the project "Probabilistic machine learning" from Nikolaus Umlauf. The Project is carried out with the support of Johannes Seiler, Stefan Lang and Kenneth Hartgen (ETH Zürich).

This project aims to better explain the problems of childhood malnutrition in low- and middle-income countries through probabilistic machine learning and to contribute to the monitoring of the Sustainable Development Goals (SGD), proposed at the United Nations Conference on Sustainable Development in Rio de Janeiro in 2012.

Recent literature emphasizes the high heterogeneity at both the national and sub-national level, and focuses on identifying drivers of malnutrition with flexible regression models. While incorporating complex modelling approaches, the applied methods are not sufficient to account for all important interactions, i.e., certain factors remain undetected, that could make a significant contribution to the overall situation. We aim to significantly improve monitoring through: (a) an improved database and (b) development of new algorithms for non- standard interactions that will be embedded in the framework of fully probabilistic distributional regression models.

The novel algorithms will be based on ideas from machine learning such as decision trees (and random decision forests) and stochastic gradient descent type algorithms, suitable for very large data sets.

The presented methods can be used for a variety of applications. The modeling approach focuses on the decomposition into main effects and (possibly) complex but interpretable interactions. The new algorithms are extremely memory efficient (including variable selection) and can be applied to virtually any number of observations on a conventional computer. With the methods developed so far, it is not possible to compute such large probabilistic models. Therefore the methods are also very useful for other applications, e.g., in the field of meteorology, real estate modelling, ecology, medicine, etc.



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