Derivative-based regularization for regression

arXiv:2405.00555v1 Announce Type: new
Abstract: In this work, we introduce a novel approach to regularization in multivariable regression problems. Our regularizer, called DLoss, penalises differences between the model's derivatives and derivatives of the data generating function as estimated from the training data. We call these estimated derivatives data derivatives. The goal of our method is to align the model to the data, not only in terms of target values but also in terms of the derivatives involved. To estimate data …

abstract arxiv call cs.lg data derivatives differences function novel regression regularization training training data type work