CRISP: Consensus Regularized Selection based Prediction

Integrating regularization methods with standard loss functions such as the least squares, hinge loss, etc., within a regression framework has become a popular choice for researchers to learn predictive models with lower variance and better generalization ability. Regularizers also aid in building interpretable models with high-dimensional data which makes… (More)