A Rigorous, Tractable Measure of Model Complexity
An accurate assessment of a model's complexity is crucial for topics such as interpretation, generalization, and model selection. However, most existing complexity measures either rely on heuristic assumptions or are computationally prohibitive. In this paper, we present a mathematically rigorous yet easy-to-compute measure of model complexity that is based on the similarities between the model gradients across inputs. It is thus well-defined for any parametric model, but also for kernel-based non-parametric models. We prove that our measure of complexity generalizes model-specific complexity measures such as polynomial degree (for polynomial regression), kernel length scale (for Matérn k...
| Authors | Oskar Allerbo, Thomas B. Schön |
|---|---|
| Primary category | stat.ML |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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