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Documents authored by Moddemann, Lukas

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DOI: 10.4230/OASIcs.DX.2024.7
Design Principles for Falsifiable, Replicable and Reproducible Empirical Machine Learning Research

Authors: Daniel Vranješ, Jonas Ehrhardt, René Heesch, Lukas Moddemann, Henrik Sebastian Steude, and Oliver Niggemann

Published in: OASIcs, Volume 125, 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)

Abstract
Machine learning is becoming increasingly important in the diagnosis and planning fields, where data-driven models and algorithms are being employed as alternatives to traditional first-principle approaches. Empirical research plays a fundamental role in the machine learning domain. At the heart of impactful empirical research lies the development of clear research hypotheses, which then shape the design of experiments. The execution of experiments must be carried out with precision to ensure reliable results, followed by statistical analysis to interpret these outcomes. This process is key to either supporting or refuting initial hypotheses. Despite its importance, there is a high variability in research practices across the machine learning community and no uniform understanding of quality criteria for empirical research. To address this gap, we propose a model for the empirical research process, accompanied by guidelines to uphold the validity of empirical research. By embracing these recommendations, greater consistency, enhanced reliability and increased impact can be achieved.
Cite as

Daniel Vranješ, Jonas Ehrhardt, René Heesch, Lukas Moddemann, Henrik Sebastian Steude, and Oliver Niggemann. Design Principles for Falsifiable, Replicable and Reproducible Empirical Machine Learning Research. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 7:1-7:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@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}
}
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