Description

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.