Escaping Parameter Space: Tight Generalization Bounds via Representation Quality

Niclas Göring; Shuofeng Zhang; Branton DeMoss; Ard A. Louis

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arXiv · 2610.23729

Escaping Parameter Space: Tight Generalization Bounds via Representation Quality

Niclas Göring·Shuofeng Zhang·Branton DeMoss·Ard A. Louis

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Abstract

We derive a tight generalization bound for deterministic neural We derive a tight generalization bound for deterministic neural networks based on their last hidden layer representations. By evaluating representation quality with a parameter-free $k$-nearest-neighbor ($k$-NN) classifier, our certificate avoids traditional parameter-space complexity measures, KL divergences to a posterior, weight norms, and compression. The bound is tight: trained from scratch, it reaches an 8.0 percentage point gap to test error on CIFAR-10 (ResNet-18) and yields the first non-vacuous certificates we are aware of on CIFAR-100; with frozen DINOv2 representations, it certifies at 1.4\% on CIFAR-10 and 23.1\% on 1,000-class ImageNet. It decomposes additively into three empirical, independently measurable quantities: local class geometry ($k$-NN decoder error), decoder-network agreement, and training stability. This allows the bound to act as a diagnostic tool: we demonstrate that actively destroying local class geometry eliminates generalization while leaving dataset memorization intact, identifying class geometry as a causally relevant mechanism for generalization. Finally, we show that learned representations are significantly less sensitive to weight noise than linear readouts, suggesting why parameter-space certificates often struggle to remain tight.

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BibTeX RIS

Niclas Göring, Shuofeng Zhang, Branton DeMoss, Ard A. Louis. 2026-10-16. Escaping Parameter Space: Tight Generalization Bounds via Representation Quality. https://arxiv.org/abs/2610.23729

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