In recent years, machine learning has shown great promise in a variety of real-world applications.

For a subclass of models, a finite number of densities suffice.

Agent-based models (ABMs) are proliferating as decision-making tools across policy areas in transportation, economics, and epidemiology.

Existing regularization methods merely minimize the norms of embeddings to regularize the model, leading to suboptimal performance.