Towards Inferential Reproducibility of Machine Learning Research
This addresses the issue of inferential reproducibility for machine learning researchers, offering a method to handle inherent noise rather than removing it, which is incremental but improves statistical rigor in evaluations.
The paper tackles the problem of unreliable machine learning evaluation due to nondeterministic noise by proposing to incorporate multiple variance sources, including interactions with data properties, into statistical analysis using linear mixed effects models and variance component analysis, enabling inference beyond specific model instances and quantifying reliability through a coefficient.
Reliability of machine learning evaluation -- the consistency of observed evaluation scores across replicated model training runs -- is affected by several sources of nondeterminism which can be regarded as measurement noise. Current tendencies to remove noise in order to enforce reproducibility of research results neglect inherent nondeterminism at the implementation level and disregard crucial interaction effects between algorithmic noise factors and data properties. This limits the scope of conclusions that can be drawn from such experiments. Instead of removing noise, we propose to incorporate several sources of variance, including their interaction with data properties, into an analysis of significance and reliability of machine learning evaluation, with the aim to draw inferences beyond particular instances of trained models. We show how to use linear mixed effects models (LMEMs) to analyze performance evaluation scores, and to conduct statistical inference with a generalized likelihood ratio test (GLRT). This allows us to incorporate arbitrary sources of noise like meta-parameter variations into statistical significance testing, and to assess performance differences conditional on data properties. Furthermore, a variance component analysis (VCA) enables the analysis of the contribution of noise sources to overall variance and the computation of a reliability coefficient by the ratio of substantial to total variance.