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