@InProceedings{vranjes_et_al:OASIcs.DX.2024.7,
  author =	{Vranje\v{s}, Daniel and Ehrhardt, Jonas and Heesch, Ren\'{e} and Moddemann, Lukas and Steude, Henrik Sebastian and Niggemann, Oliver},
  title =	{{Design Principles for Falsifiable, Replicable and Reproducible Empirical Machine Learning Research}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{7:1--7:13},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-356-0},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{125},
  editor =	{Pill, Ingo and Natan, Avraham and Wotawa, Franz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.7},
  URN =		{urn:nbn:de:0030-drops-220991},
  doi =		{10.4230/OASIcs.DX.2024.7},
  annote =	{Keywords: machine learning, hypothesis design, research design, experimental research, statistical testing, diagnosis, planning}
}