Abstract: A computer-implemented method of automating inductive bias selection includes a computer receiving a plurality of examples, each example providing a plurality of feature-value pairs. The computer constructs an inductive bias dataset which correlates each respective example in the plurality of examples with numerical indications of training quality. The numerical indications of training quality for each respective example are generated by creating a plurality of models, with each model corresponding to a distinct set of inductive biases. The training quality for each respective model is evaluated when applied to the respective example. The computer uses the inductive bias dataset to select a plurality of inductive biases for application to one or more new datasets.

Abstract: A computer-implemented method for scoring an example with causal information includes a computer system receiving an indication of a goal and applying a predictive model to the example to yield an output score with respect to the goal. The computer system produces causal score for each feature-value pair in the example, each causal score indicating of a relative extent to which the feature-value pair is responsible for influencing the output score. Then, the computer system presents the output score and the causal score for each feature-value pair in the example.

Abstract: A computer-implemented method for profiling a population of examples includes a computer system creating a rule collection comprising a plurality of rules, wherein each rule describes a respective corresponding sub-population of the examples according to a conjunction of a plurality of feature-value pairs. The computer system generates a precisely descriptive profile by performing a search process on the rule collection to identify a rule that either maximizes or minimizes the value of a user-specified target feature in the respective corresponding sub-population.

Abstract: A computer-implemented method for scoring an example with causal information includes a computer system receiving an indication of a goal and applying a predictive model to the example to yield an output score with respect to the goal. The computer system produces causal score for each feature-value pair in the example, each causal score indicating of a relative extent to which the feature-value pair is responsible for influencing the output score. Then, the computer system presents the output score and the causal score for each feature-value pair in the example.

Abstract: A computer-implemented method of automating inductive bias selection includes a computer receiving a plurality of examples, each example providing a plurality of feature-value pairs. The computer constructs an inductive bias dataset which correlates each respective example in the plurality of examples with numerical indications of training quality. The numerical indications of training quality for each respective example are generated by creating a plurality of models, with each model corresponding to a distinct set of inductive biases. The training quality for each respective model is evaluated when applied to the respective example. The computer uses the inductive bias dataset to select a plurality of inductive biases for application to one or more new datasets.

Abstract: A device for performing autonomous analytics comprises one or more adaptors, device hierarchy data, and an analytic executive. The adaptors are configured to adapt data streams from one or more heterogeneous data sources into a tagged dataset. The device hierarchy data comprises an identification of one or more hierarchical relationships between the device and one or more additional devices. The analytic executive is configured to identify a plurality of relevant devices based on the device hierarchy data and collect device data from each of the plurality of relevant devices. The analytic executive is further configured to generate a collection of analytic models using the collected device data, score one or more new data items included in the tagged dataset using the collection of analytic models, yielding scored results, and use one or more business rules to trigger an action based on the scored results.

Abstract: A method for profiling a population of examples includes a computer receiving a dataset representative of the population of examples, a user selection of a population constraint, and an indication of a goal. The computer generates shallow fixed-depth trees based on the dataset and determines a collection of leaves of the shallow fixed-depth trees meeting the population constraint. Next, the computer sorts the collection of leaves based on a degree to which the goal is met. Then, the computer creates one or more profiles based on the collection of leaves.