An Analysis of Regularized Approaches for Constrained Machine Learning

Understand

Regularization-based approaches for injecting constraints in Machine Learning (ML) were introduced to improve a predictive model via expert knowledge.

• We tackle the issue of finding the right balance between the loss (the accuracy of the learner) and the regularization term (the degree of constraint satisfaction).
• The key results of this paper is the formal demonstration that this type of approach cannot guarantee to find all optimal solutions.
• In particular, in the non-convex case there might be optima for the constrained problem that do not correspond to any multiplier value.