Position: Why We Must Rethink Empirical Research in Machine Learning
<|reference_start|>Position: Why We Must Rethink Empirical Research in Machine Learning: 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 undermine progress in the field. To overcome this alarming situation, we call for more awareness of the plurality of ways of gaining knowledge experimentally but also of some epistemic limitations. In particular, we argue most current empirical machine learning research is fashioned as confirmatory research while it should rather be considered exploratory.<|reference_end|>
@article{herrmann2024position:,
    title={Position: Why We Must Rethink Empirical Research in Machine Learning},
    author={Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe
  Casalicchio, Marcel Wever, Matthias Feurer, David R"ugamer, Eyke
  H"ullermeier, Anne-Laure Boulesteix, Bernd Bischl},
    journal={arXiv preprint arXiv:2405.02200},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02200},
    primaryClass={cs.LG stat.ML}
}