Learning Model Constraints for Structured Prediction – LearnCost

Structured output prediction based on discriminatively trained probabilistic graphical models is a powerful framework that has lead to a large improvement in predictive systems in the past decade. These models, however, often require strong a priori constraints to guarantee tractable inference procedures. These constraints can limit the power of the model to provide good predictions, and can therefore be viewed as a necessary evil.

This project will develop statistical learning tools for structured output prediction that incorporate model constraints to ensure low-order polynomial time complexity of the inference procedure. Furthermore, these constraints will be learned from training data to maximize the expressivity of the model class while probabilistically enforcing efficient inference in a fashion that is adaptive to the specific problem instance.