Software

EarlyStopping   

Together with Eric Ziebell, Ratmir Miftachov and Bernhard Stankewitz, we develop the Python library EarlyStopping implementing computationally efficient model selection methods.

For iterative estimation procedures applied to statistical learning problems, it is necessary to choose a suitable iteration index to avoid under- and overfitting. Classical model selection criteria can be prohibitively expensive in high dimensions. Recently, it has been shown for several regularisation methods that sequential early stopping can achieve statistical and computational efficiency by halting at a data-driven index depending on previous iterates only.

You can view the documentation and the preprint for the package here and here, respectively.