Position: Why We Must Rethink Empirical Research in Machine Learning

Herrmann, Moritz ; Lange, F. Julian D.; Eggensperger, Katharina; Casalicchio, Giuseppe; Wever, Marcel ; Feurer, Matthias; Rügamer, David ; Hüllermeier, Eyke ; Boulesteix, Anne-Laure und Bischl, Bernd (July 2024): Position: Why We Must Rethink Empirical Research in Machine Learning. 41st International Conference on Machine Learning (ICML 2024), Vienna, Austria, 21. - 27. July 2024. In: Proceedings of the 41st International Conference on Machine Learning, Proceedings of Machine Learning Research Vol. 235 PMLR. pp. 18228-18247

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.

• Item Type: Conference or Workshop Item (Paper)
• Faculties: Mathematics, Computer Science and Statistics > Computer Science; Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 000 Computer science, knowledge, and systems
• ISSN: 2640-3498
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - Unspecified
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 390727645