厘清机器学习领域对'可复现性'的多重含义，提出八大研究方向。
What Do Machine Learning Researchers Mean by "Reproducible"?
- 梳理社区对可复现性的理解，归纳为八个核心主题。
- 发现许多相关工作未自称为可复现研究，因早于该议题流行。
- 适合关注实验透明性与科研严谨性的研究人员参考。
人工智能与机器学习领域日益加剧的'可复现性危机'引发广泛关注。然而，每篇论文中'可复现性'的具体含义常不明确。本文旨在澄清该概念在学术界中的实际内涵，提出将相关研究划分为八个普遍主题。在此框架下，我们发现这些主题下包含大量未主动标注为'可复现性'的研究，部分甚至早在该问题引起广泛关注前几十年便已存在。
原文摘要 · Abstract (English)
The concern that Artificial Intelligence (AI) and Machine Learning (ML) are entering a "reproducibility crisis" has spurred significant research in the past few years. Yet with each paper, it is often unclear what someone means by "reproducibility". Our work attempts to clarify the scope of "reproducibility" as displayed by the community at large. In doing so, we propose to refine the research to eight general topic areas. In this light, we see that each of these areas contains many works that do not advertise themselves as being about "reproducibility", in part because they go back decades before the matter came to broader attention.
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