arXiv:2508.04409v3 Announce Type: replace-cross Abstract: Cross-validation (CV) is known to provide asymptotically exact tests and confidence intervals for model improvement but only when the model comparison is relatively stable. Surprisingly, we prove that even simple, individually stable models can generate relatively unstable comparisons, calling into question the validity of CV inference. Specifically, we show that the Lasso and its close cousin, soft-thresholding, generate relatively unstable comparisons and invalid CV inferences, even in the most favorable of learning settings and when
The paper highlights a fundamental issue with cross-validation in model comparison, which is a widely used and often assumed-valid technique in contemporary machine learning development.
This research challenges a foundational methodology in AI/ML, suggesting that some widely accepted model comparison results and subsequent deployments might be built on unstable ground, potentially leading to suboptimal or incorrect conclusions.
The understanding of cross-validation's reliability for model comparison is altered, requiring more rigorous validation or alternative methods, particularly for sensitive applications.
- · Researchers developing new model validation techniques
- · AI safety and interpretability specialists
- · Developers of more robust statistical methods
- · ML practitioners over-relying on standard cross-validation
- · Models whose efficacy was primarily validated through unstable cross-validation
- · AI products built on such potentially invalid inferences
Increased scrutiny and demand for more robust model comparison and validation techniques in machine learning.
A potential re-evaluation of past research findings or deployed AI systems that relied heavily on these 'unstable' cross-validation methods.
Investment in developing and standardizing alternative or enhanced validation frameworks to ensure reliable AI performance and safety.
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Read at arXiv cs.LG