Position: Let's Strengthen Verifiability If We Can't Enforce Reproducibility

Abstract:In the field of Machine Learning, many papers contain empirical results supporting claimed statements or illustrating the performance of a proposed method. However, most practitioners know that (1) results are generally hard to reproduce, and increasingly so, (2) code is not often available to do so, and (3) it hinders the development of research. In this position paper, we analyze and quantify these issues, and make concrete proposals to improve result checkability, if not reproducibility. Code and supporting materials are available at this https URL.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)

arXiv:2609.35854 [cs.LG]

(or arXiv:2609.35854v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.35854 Focus to learn more arXiv-issued DOI via DataCite

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