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Sources of Irreproducibility in Machine Learning: A Review

2022-04-15 18:26:03
Odd Erik Gundersen, Kevin Coakley, Christine Kirkpatrick
arXiv_AI
   
Abstract

Lately, several benchmark studies have shown that the state of the art in some of the sub-fields of machine learning actually has not progressed despite progress being reported in the literature. The lack of progress is partly caused by the irreproducibility of many model comparison studies. Model comparison studies are conducted that do not control for many known sources of irreproducibility. This leads to results that cannot be verified by third parties. Our objective is to provide an overview of the sources of irreproducibility that are reported in the literature. We review the literature to provide an overview and a taxonomy in addition to a discussion on the identified sources of irreproducibility. Finally, we identify three lines of further inquiry.

Abstract (translated)

URL

https://arxiv.org/abs/2204.07610

PDF

https://arxiv.org/pdf/2204.07610.pdf

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