Position: Why We Must Rethink Empirical Research in Machine Learning

Moritz Herrmann; F. Julian D. Lange; Katharina Eggensperger; Giuseppe Casalicchio; Marcel Wever; Matthias Feurer; David Rügamer; Eyke Hüllermeier; Anne-Laure Boulesteix; Bernd Bischl

doi:10.48550/arXiv.2405.02200

Position: Why We Must Rethink Empirical Research in Machine Learning

Moritz Herrmann
, F. Julian D. Lange
, Katharina Eggensperger
, Giuseppe Casalicchio
, Marcel Wever
, Matthias Feurer
, David Rügamer
, Eyke Hüllermeier
, Anne-Laure Boulesteix
, Bernd Bischl

Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review

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.

Original language	English
Title of host publication	Proceedings of the international conference on machine learning
DOIs	https://doi.org/10.48550/arXiv.2405.02200
Publication status	Published - 2024
Externally published	Yes

Access to Document

10.48550/arXiv.2405.02200Licence: CC BY 4.0

Cite this

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title = "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.",

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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

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PY - 2024

Y1 - 2024

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.

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BT - Proceedings of the international conference on machine learning

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