2 papers
stat.CO2025
Spectrally Deconfounded Random Forests
Markus Ulmer, Cyrill Scheidegger, Peter Bühlmann
We introduce a modification of Random Forests to estimate functions when unobserved confounding variables are present. The technique is tailored for high-dimensional settings with…
stat.ML2019
Change point detection for graphical models in the presence of missing values
Malte Londschien, Solt Kovács, Peter Bühlmann
We propose estimation methods for change points in high-dimensional covariance structures with an emphasis on challenging scenarios with missing values. We advocate three imputatio…