Position: Why We Must Rethink Empirical Research in Machine Learning
Abstract
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.
Cite
Text
Herrmann et al. "Position: Why We Must Rethink Empirical Research in Machine Learning." International Conference on Machine Learning, 2024.Markdown
[Herrmann et al. "Position: Why We Must Rethink Empirical Research in Machine Learning." International Conference on Machine Learning, 2024.](https://mlanthology.org/icml/2024/herrmann2024icml-position/)BibTeX
@inproceedings{herrmann2024icml-position,
title = {{Position: Why We Must Rethink Empirical Research in Machine Learning}},
author = {Herrmann, Moritz and Lange, F. Julian D. and Eggensperger, Katharina and Casalicchio, Giuseppe and Wever, Marcel and Feurer, Matthias and Rügamer, David and Hüllermeier, Eyke and Boulesteix, Anne-Laure and Bischl, Bernd},
booktitle = {International Conference on Machine Learning},
year = {2024},
pages = {18228-18247},
volume = {235},
url = {https://mlanthology.org/icml/2024/herrmann2024icml-position/}
}