These differences suggest that the constraint selection algorithms allow label regularization to be more robust to noisy and conflicting constraints.
using constraint selection, we can view constraint engineering akin to feature engineering in discriminative, supervised learning methods -developers can add many types of constraints to the model without (much) fear of reducing accuracy.