mlsanityData to 14 Oct 2026

In Defense of the Paper

arXiv 2104.08359 ↗First posted 16 Apr 2021cs.LG cs.DLcited by 0

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Abstract (arXiv)

The machine learning publication process is broken, of that there can be no doubt. Many of these flaws are attributed to the current workflow: LaTeX to PDF to reviewers to camera ready PDF. This has understandably resulted in the desire for new forms of publications; ones that can increase inclusively, accessibility and pedagogical strength. However, this venture fails to address the origins of these inadequacies in the contemporary paper workflow. The paper, being the basic unit of academic research, is merely how problems in the publication and research ecosystem manifest; but is not itself responsible for them. Not only will simply replacing or augmenting papers with different formats not fix existing problems; when used as a band-aid without systemic changes, will likely exacerbate the existing inequities. In this work, we argue that the root cause of hindrances in the accessibility of machine learning research lies not in the paper workflow but within the misaligned incentives behind the publishing and research processes. We discuss these problems and argue that the paper is the optimal workflow. We also highlight some potential solutions for the incentivization problems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 51 references that are in the map. First posted on arXiv 16 Apr 2021.

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References · 20

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8 more
  1. 2019Differential Privacy Has Disparate Impact on Model Accuracycited by 316
  2. 2020Characterising Bias in Compressed Modelscited by 167
  3. 2020Maxmin Q-learning: Controlling the Estimation Bias of Q-learningcited by 117
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  8. 2019HARK Side of Deep Learning -- From Grad Student Descent to Automated Machine Learningcited by 13

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