TY - CHAP

T1 - Position: Why We Must Rethink Empirical Research in Machine Learning

AU - Herrmann, Moritz

AU - Lange, F. Julian D.

AU - Eggensperger, Katharina

AU - Casalicchio, Giuseppe

AU - Wever, Marcel

AU - Feurer, Matthias

AU - Rügamer, David

AU - Hüllermeier, Eyke

AU - Boulesteix, Anne-Laure

AU - Bischl, Bernd

N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2024/7

Y1 - 2024/7

N2 - 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.

AB - 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.

U2 - 10.48550/arXiv.2405.02200

DO - 10.48550/arXiv.2405.02200

M3 - Contribution to book/anthology

BT - Proceedings of the international conference on machine learning

ER -