# A generalization gap estimation for overparameterized models via Langevin functional variance

@article{Okuno2021AGG, title={A generalization gap estimation for overparameterized models via Langevin functional variance}, author={Akifumi Okuno and Keisuke Yano}, journal={ArXiv}, year={2021}, volume={abs/2112.03660} }

This paper discusses the estimation of the generalization gap, the difference between a generalization error and an empirical error, for overparameterized models (e.g., neural networks). We first show that a functional variance, a key concept in defining a widely-applicable information criterion, characterizes the generalization gap even in overparameterized settings where a conventional theory cannot be applied. We also propose a computationally efficient approximation of the function variance…

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