KIML: future work

Examples of valuable domain knowledge other than monotonicities include synergistic influence (two things both positively influence an outcome, but their combined effect is greater than mere additivity) and relative strength-of-influence (two things both influence an outcome, but it is known one is a significantly stronger predictor than the other). We have not yet run experiments to test the value of these statements, but we do have defined mappings from such statements to constraints on probability distributions, and we expect similar results as were obtained for monotonicities.

Longer term "knowledge-intensive machine learning" goals at Oregon State are more ambitious: automatic feature engineering, model simplification, etc.

By the way, for those of you interested in details, I can provide a current draft of my thesis (especially if you are willing to provide constructive criticism :-).