Difficulties in replication and reproducibility of empirical evidences in machine learning research have become a prominent topic in recent years. Ensuring that machine learning research results are sound and reliable requires reproducibility, which verifies the reliability of research findings using the same code and data. This promotes open and accessible research, robust experimental workflows, and the rapid integration of new findings. Evaluating the degree to which research publications support these different aspects of reproducibility is one goal of the present work. For this we introduce an ontology of reproducibility in machine learning and apply it to methods for graph neural networks. Building on these efforts we turn towards another critical challenge in machine learning, namely the curse of dimensionality, which poses challenges in data collection, representation, and analysis, making it harder to find representative data and impeding the training and inference processes. Using the closely linked concept of geometric intrinsic dimension we investigate to which extend the used machine learning models are influenced by the intrinsic dimension of the data sets they are trained on.

翻译：近年来，机器学习研究中实证结果的可复现与可重复性难题已成为一个显著议题。确保机器学习研究成果可靠且可信，需要依靠可复现性——即使用相同代码与数据验证研究发现的可靠性。这有助于推动开放可获取的研究、稳健的实验工作流程以及新成果的快速整合。评估研究出版物在多大程度上支持可复现性的不同维度，是本研究的目标之一。为此，我们提出了一种机器学习领域的可复现性本体论，并将其应用于图神经网络方法。基于这些工作，我们转向机器学习中的另一项关键挑战——维度灾难，它给数据收集、表示与分析带来困难，导致难以找到代表性数据，并阻碍训练与推理过程。通过利用与之紧密相关的几何本征维度概念，我们探究了所使用的机器学习模型在多大程度上受其训练数据集本征维度的影响。