4 citations · 10 across the 6 of their papers we have counts for
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stat.ML2021★ 1 cited
Interpretable Machines: Constructing Valid Prediction Intervals with Random Forests
Burim Ramosaj
An important issue when using Machine Learning algorithms in recent research is the lack of interpretability. Although these algorithms provide accurate point predictions for vario…
stat.ML2019
Asymptotic Unbiasedness of the Permutation Importance Measure in Random Forest Models
Burim Ramosaj, Markus Pauly
Variable selection in sparse regression models is an important task as applications ranging from biomedical research to econometrics have shown. Especially for higher dimensional r…
stat.ML2017★ 4 cited
Who wins the Miss Contest for Imputation Methods? Our Vote for Miss BooPF
Burim Ramosaj, Markus Pauly
Missing data is an expected issue when large amounts of data is collected, and several imputation techniques have been proposed to tackle this problem. Beneath classical approaches…