Abstract: One or more application domain properties are integrated into a machine learning model by obtaining training data for use in training the machine learning model, where the training data includes factual data relating to a particular application, and obtaining, with reference to the training data, unlabeled counterfactual data for the particular application. The method includes imputing one or more labels to the unlabeled counterfactual data using domain knowledge for the particular application to obtain imputed counterfactual data. The domain knowledge includes one or more application domain properties. Further, the method includes training the machine learning model using the training data and the imputed counterfactual data to facilitate generating machine learning model predictions for the particular application in accordance with the one or more application domain properties.

Abstract: A method of using a computing device to self-train a machine learning model with an incomplete dataset including original observational data. The method includes receiving a labeled training data, the labeled training data for training a machine learning model. Counterfactual unlabeled training data is received. One or more labels are predicted for the counterfactual unlabeled training data. The machine learning model is trained based upon the labeled training data, the counterfactual unlabeled training data, and the predicted one or more labels for the unlabeled training data. The machine learning model reduces bias in original observational data. An evaluation of the predicted one or more labels is received based on corresponding artificial intelligence explanations provided by an artificial intelligence explainability model.

Abstract: A machine learning system that incorporates arbitrary constraints is provided. The machine learning system selects a set of domain-specific constraints from a plurality of sets of domain-specific constraints. The machine learning system selects a set of general functional relationships from a plurality of sets of general functional relationships. The machine learning system maps the selected set of general functional relationships and the selected set of domain-specific constraints to a set of learning transforms. The machine learning system modifies a machine learning specification according to the set of learning transforms, wherein the machine learning specification specifies a model construction, a model setup, and a training objective function. The machine learning system optimizes a machine learning model according to the modified machine learning specification.

Abstract: A machine learning system that incorporates arbitrary constraints into deep learning model is provided. The machine learning system provides a set of penalty data points en a set of arbitrary constraints in addition to a set of original training data points. The machine learning system assigns a penalty to each penalty data point in the set of penalty data points. The machine learning system optimizes a machine learning model by solving an objective function based on an original loss function and a penalty loss function. The original loss function is evaluated over a set of original training data points and the penalty loss function is evaluated over the set of penalty data points. The machine learning system provides the optimized machine learning model based on a solution of the objective function.

Abstract: A machine learning system that uses a split net configuration to incorporate arbitrary constraints receives a set of input data and a set of functional constraints. The machine learning system jointly optimizes a deep learning model by using the set of input data and a wide learning model by using the set of constraints. The deep learning model includes an input layer, an output layer, and an intermediate layer between the input layer and the output layer. The wide learning model includes an input layer and an output layer but no intermediate layer. The machine learning system provides a machine learning model comprising the optimized deep learning model and the optimized wide learning model.

Abstract: One embodiment of the invention provides a method for integrated segmentation and prescriptive policies generation. The method comprises training a first artificial intelligence (AI) model and a second model based on training data. The first AI model comprises a teacher model trained to determine a likelihood of a desired outcome for a given action. The second model comprises a prescriptive tree trained for segmentation. The method further comprises determining, via the teacher model, a first policy that produces an optimal action. The optimal action provides a best expected outcome. The method further comprises applying, via the second model, a recursive segmentation algorithm to generate one or more interpretable prescriptive policies. Each interpretable prescriptive policy is less complex and more interpretable than the first policy. The method further comprises, for each interpretable prescriptive policy, determining, via the teacher model, an expected outcome for the interpretable prescriptive policy.