← 研踪 PaperTrailarXiv:2605.21167 · v2
A Rigorous, Tractable Measure of Model Complexity
首次提交 2026-05-20 21:37
版本更新 2026-11-04 22:51
雷达观测 2026-11-05 12:13
摘要
An accurate assessment of a model's complexity is crucial for topics such as interpretation, generalization, and model selection. However, 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 feature vectors for different inputs. It is thus well-defined for any parametric model (via NTK feature generalization) and for kernel-based non-parametric models (via the induced kernel). We prove that our measure of complexity generalizes model-specific complexity measures, including polynomial degree (for polynomial regression), kernel length scale (for Matérn kernels), number of neighbors (for k-nearest neighbors), number of splits (for decision trees), and number of trees (for random forests). We also use our measure to obtain new insights into the double descent phenomenon for random Fourier features, random forests, neural networks, and gradient boosting.
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v2 · 2026-11-04 22:51 · 最新观测版本
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, 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 feature vectors for different inputs. It is thus well-defined for any parametric model (via NTK feature generalization) and for kernel-based non-parametric models (via the induced kernel). We prove that our measure of complexity generalizes model-specific complexity measures, including polynomial degree (for polynomial regression), kernel length scale (for Matérn kernels), number of neighbors (for k-nearest neighbors), number of splits (for decision trees), and number of trees (for random forests). We also use our measure to obtain new insights into the double descent phenomenon for random Fourier features, random forests, neural networks, and gradient boosting.
查看此版本解读 →查看此版本原文 ↗