4 citations · 9 across the 6 of their papers we have counts for
6 papers
Advancing Trustworthy AI in Healthcare Through Meta-Research: Results of an Interdisciplinary Design-Thinking Workshop
Valerie Bürger, Marlie Besouw, Jana Fehr +26
Meta-research and Trustworthy AI (TAI) share common goals, namely improving evidence, robustness, and transparency, yet there is very little interplay between the two fields. To in…
Overcoming Barriers to Computational Reproducibility
Roman Hornung, László Németh, Oleksandr Zadorozhny +13
Computational reproducibility, the possibility for independent researchers to exactly reproduce published empirical results, is fundamental to science. Despite its importance, the…
On "Confirmatory" Methodological Research in Statistics and Related Fields
F. J. D. Lange, Juliane C. Wilcke, Sabine Hoffmann +2
Empirical substantive research, such as in the life or social sciences, is commonly categorized into the two modes exploratory and confirmatory, both of which are essential to scie…
Rethinking the handling of method failure in comparison studies
Milena Wünsch, Moritz Herrmann, Elisa Noltenius +3
Comparison studies in methodological research are intended to compare methods in an evidence-based manner to help data analysts select a suitable method for their application. To p…
Position: Why We Must Rethink Empirical Research in Machine Learning
Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger +7
We warn against a common but incomplete understanding of empirical research in machine learning that leads to non-replicable results, makes findings unreliable, and threatens to un…
To tweak or not to tweak. How exploiting flexibilities in gene set analysis leads to over-optimism
Milena Wünsch, Christina Sauer, Moritz Herrmann +2
Gene set analysis, a popular approach for analysing high-throughput gene expression data, aims to identify sets of genes that show enriched expression patterns between two conditio…