Publications

Preprints

K. Blesch, N. Koenen, J. Kapar, P. Golchian, L. Burk, M. Loecher, M. N. Wright, “Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests.” arXiv,Jan. 19, 2025. doi: 10.48550/arXiv.2501.11178.
L. Burk, J. Zobolas, B. Bischl, A. Bender, M. N. Wright, and R. Sonabend, “A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data.” arXiv, Jun. 06, 2024. doi: 10.48550/arXiv.2406.04098.
R. Sonabend, J. Zobolas, P. Kopper, L. Burk, and A. Bender, “Examining properness in the external validation of survival models with squared and logarithmic losses.” arXiv, Jun. 03, 2024. doi: 10.48550/arXiv.2212.05260.

L. Burk, A. Bender, and M. N. Wright, “High-Dimensional Variable Selection With Competing Events Using Cooperative Penalized Regression”, Biometrical Journal, vol. 67, no. 1, p. e70036, 2025, doi: 10.1002/bimj.70036.
H. J. Coyle-Asbil, L. Burk et al., “Energy expenditure prediction in preschool children: a machine learning approach using accelerometry and external validation” Physiol. Meas., vol. 45, no. 9, p. 095015, Sep. 2024, doi: 10.1088/1361-6579/ad7ad2.
M. Heckmann and L. Burk, “Gridsampler – A Simulation Tool to Determine the Required Sample Size for Repertory Grid Studies.” JORS, vol. 5, no. 1, p. 2, Jan. 2017, doi: 10.5334/jors.150.

G. Casalicchio and L. Burk, “Evaluation and Benchmarking.” in Applied machine learning using mlr3 in R , 1st ed., B. Bischl, R. Sonabend, L. Kotthoff, and M. Lang, Eds., CRC Press, 2024. Available: https://mlr3book.mlr-org.com/evaluation_and_benchmarking.html