Beside the minimizationof the prediction error, two of the most desirable properties of a regression scheme are stability and interpretability . Driven by these principles, we propose continuous-domain formulations for one-dimensional regression problems. In our first approach, we use the Lipschitz constant as a regularizer, which results in an implicit tuning of the overall robustness of the learned mapping.