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arXiv · 2503.16753

EarlyStopping: Implicit Regularization for Iterative Learning Procedures in Python

Eric Ziebell·Ratmir Miftachov·Bernhard Stankewitz·Laura Hucker
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Abstract

Iterative learning procedures are ubiquitous in machine learning and modern statistics. Regularision is typically required to prevent inflating the expected loss of a procedure in later iterations via the propagation of noise inherent in the data. Significant emphasis has been placed on achieving this regularisation implicitly by stopping procedures early. The EarlyStopping-package provides a toolbox of (in-sample) sequential early stopping rules for several well-known iterative estimation procedures, such as truncated SVD, Landweber (gradient descent), conjugate gradient descent, L2-boosting and regression trees. One of the central features of the package is that the algorithms allow the specification of the true data-generating process and keep track of relevant theoretical quantities. In this paper, we detail the principles governing the implementation of the EarlyStopping-package and provide a survey of recent foundational advances in the theoretical literature. We demonstrate how to use the EarlyStopping-package to explore core features of implicit regularisation and replicate results from the literature.

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Eric Ziebell, Ratmir Miftachov, Bernhard Stankewitz, Laura Hucker. 2025-03-20. EarlyStopping: Implicit Regularization for Iterative Learning Procedures in Python. https://arxiv.org/abs/2503.16753

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