Machine Learning
This group focuses on methodological research and application of Machine Learning methods
-
Extension of existing methods on causal personalized treatment effect estimation with model-based trees and random forests, and to provide user-friendly and very flexible R implementations for extensible trees and forests
-
Machine Learning methods for time-to-event data analysis (survival analysis) that go beyond right-censoring and proportional hazards
-
Variants of (component-wise) gradient boosting that facilitate interpretability
-
Combining ML techniques with geometric numerical methods in phase space with the goal to develop physics-informed machine learning methods. Reduced complexity models as emulators or surrogates that retain mathematical and physical properties of the underlying system (collaboration with the Max Planck Institute for Plasma Physics).
-
Applications of Machine Learning in substantive sciences like medicine and psychology
-
Providing state of the art software implementation and their integration within a unified framework for Machine Learning using R
Projects and Software
-
mlr3
: Efficient, object-oriented programming on the building blocks of machine learning -
mlr3proba
: Probabilistic Supervised Learning formlr3
-
compboost
: Fast and Flexible Component-Wise Boosting Framework -
partykit
: A toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models -
model4you
: Model-based trees and random forest for personalized treatment effect estimation -
palmtree
: Partially Additive (Generalized) Linear Model Trees / PALM trees
Members
Name | Position | |||
---|---|---|---|---|
Dr. David Rügamer | PostDoc | |||
Dr. Andreas Bender | PostDoc | |||
Dr. Ludwig Bothmann | PostDoc | |||
Dr. Susanne Dandl | PostDoc | |||
Daniel Schalk | PhD Student | |||
Stefan Coors | PhD Student | |||
Christian A. Scholbeck | PhD Student | |||
Julia Terhart | PhD Student | |||
Katharina Rath | PhD Student | |||
Theresa Stüber | PhD Student | |||
Chris Kolb | PhD Student |