Abstract: A method for computing possibly optimal policies in reinforcement learning with multiple objectives and tradeoffs includes receiving a dataset comprising state, action, and reward information for objectives in a multiple objective environment. Tradeoff information indicating that a first vector comprising first values of the objectives in the multiple objective environment is preferred to a second vector comprising second values of the objectives in the multiple objective environment is received. A set of possibly optimal policies for the multiple objective environment is produced based on the dataset and the tradeoff information, where the set of possibly optimal policies indicates actions for an intelligent agent operating in the multiple objective environment to take.

Abstract: Various embodiments are provided for accelerating machine learning in a computing environment by one or more processors in a computing system. Selected data may be received for training machine learning pipelines. Each of the machine learning pipelines may be scored according to one or more learning curves while training on selected data. Completion of the training on the selected data may be permitted for those of the machine learning pipelines having a score greater than a selected threshold. The training on the selected data may be terminated, prior to completion, on those of the machine learning pipelines having a score less than a selected threshold.

Abstract: A method for computing possibly optimal policies in reinforcement learning with multiple objectives and tradeoffs includes receiving a dataset comprising state, action, and reward information for objectives in a multiple objective environment. Tradeoff information indicating that a first vector comprising first values of the objectives in the multiple objective environment is preferred to a second vector comprising second values of the objectives in the multiple objective environment is received. A set of possibly optimal policies for the multiple objective environment is produced based on the dataset and the tradeoff information, where the set of possibly optimal policies indicates actions for an intelligent agent operating in the multiple objective environment to take.

Abstract: An input model can be received, along with a set of requirements. The set of requirements may describe an output model to be trained. The output model can then be trained. The training of the output model can be based on the input model and based further on at least one intermediate model.

Abstract: According to one embodiment, a method, computer system, and computer program product for reinforcement learning is provided. The present invention may include training, using an offline dataset, a plurality of diverse reward models, and creating a policy based on an output of the reward models and a robustness operator of the reward models.

Abstract: An output layer is removed from a pre-trained neural network model and a neural capacitance probe unit with multiple layers is incorporated on top of one or more bottom layers of the pre-trained neural network model. The neural capacitance probe unit is randomly initialized and a modified neural network model is trained by fine-tuning the one or more bottom layers on a target dataset for a maximum number of epochs, the modified neural network model comprising the neural capacitance probe unit incorporated with multiple layers on top of the one or more bottom layers of the pre-trained neural network model. An adjacency matrix is obtained from the initialized neural capacitance probe unit and a neural capacitance metric is computed using the adjacency matrix. An active model is selected using the neural capacitance metric and a machine learning system is configured using the active model.

Abstract: A method, a neural network, and a computer program product are provided that provide training of neural networks with continued fractions architectures. The method includes receiving, as input to a neural network, input data and training the input data through a plurality of continued fractions layers of the neural network to generate output data. The input data is provided to each of the continued fractions layers as well as output data from a previous layer. The method further includes outputting, from the neural network, the output data. Each continued fractions layer of the continued fractions layers is configured to calculate one or more linear functions of its respective input and to generate an output that is used as the input for a subsequent continued fractions layer, each continued fractions layer configured to generate an output that is used as the input for a subsequent layer.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate inter-operator backpropagation in AutoML frameworks are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components comprise a selection component that selects a subset of deep learning and non-deep learning operators. The computer executable components further comprise a training component which trains the subset of deep learning and non-deep learning operators, wherein deep learning operators in the subset of deep learning and non-deep learning operators are trained using backpropagation across at least two deep learning operators of the subset of deep learning and non-deep learning operators.

Abstract: A method, system, and computer program product, including generating a contrastive explanation for a decision of a classifier trained on structured data, highlighting an important feature that justifies the decision, and determining a minimal set of new values for features that alter the decision.

Abstract: Computerized interactive feature visualization is carried out on a data set—a plurality of insight classes rank a plurality of features of the data set. Via a computerized user interface, user feedback is obtained based on the interactive feature visualization—a user selects and ranks a subset of the features. At least one transformation function is applied to at least one feature of the subset of features selected by the user, to automatically construct, with a computer, at least one additional feature for the data set. The data set with the at least one additional feature is a transformed data set. In some cases, a supervised task is carried out on the final data set; accuracy of a machine learning system implementing the at least one supervised task can be enhanced by the at least one additional feature, and/or a physical system can be controlled based on results of the at least one supervised task.

Abstract: Various embodiments are provided for accelerating machine learning in a computing environment by one or more processors in a computing system. Selected data may be received for training machine learning pipelines. Each of the machine learning pipelines may be scored according to one or more learning curves while training on selected data. Completion of the training on the selected data may be permitted for those of the machine learning pipelines having a score greater than a selected threshold. The training on the selected data may be terminated, prior to completion, on those of the machine learning pipelines having a score less than a selected threshold.

Abstract: A computer automatically selects a machine learning model pipeline using a meta-learning machine learning model. The computer receives ground truth data and pipeline preference metadata. The computer determines a group of pipelines appropriate for the ground truth data, and each of the pipelines includes an algorithm. The pipelines may include data preprocessing routines. The computer generates hyperparameter sets for the pipelines. The computer applies preprocessing routines to ground truth data to generate a group of preprocessed sets of said ground truth data and ranks hyperparameter set performance for each pipeline to establish a preferred set of hyperparameters for each of pipeline. The computer selects favored data features and applies each of the pipelines, with associated sets of preferred hyperparameters, to score the favored data features of the preprocessed ground truth data. The computer ranks pipeline performance and selects a candidate pipeline according to the ranking.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory comprising instructions, which are executed by the at least one processor and configure the processor to implement a patient information extractor. The patient information extractor receives a query specification for executing a query on a patient electronic medical record (EMR). The query specification provides parameters indicating a methodology for extracting search results from the patient EMR. The patient information extractor retrieves the patient EMR from a patient registry. The patient information extractor automatically executes the query specification on the retrieved patient EMR to thereby extract the search results from the patient EMR in accordance with the parameters of the query specification. The patient information extractor automatically processes the extracted search results to generate a patient indicator value.

Abstract: Computerized interactive feature visualization is carried out on a data set—a plurality of insight classes rank a plurality of features of the data set. Via a computerized user interface, user feedback is obtained based on the interactive feature visualization—a user selects and ranks a subset of the features. At least one transformation function is applied to at least one feature of the subset of features selected by the user, to automatically construct, with a computer, at least one additional feature for the data set. The data set with the at least one additional feature is a transformed data set. In some cases, a supervised task is carried out on the final data set; accuracy of a machine learning system implementing the at least one supervised task can be enhanced by the at least one additional feature, and/or a physical system can be controlled based on results of the at least one supervised task.

Abstract: A method, system, and computer program product, including generating a contrastive explanation for a decision of a classifier trained on structured data, highlighting an important feature that justifies the decision, and determining a minimal set of new values for features that alter the decision.

Abstract: A machine-learning model generation method, system, and computer program product deciding, via a first algorithm, a machine-learning algorithm that is best for customer data, invoking the machine-learning algorithm to train a neural network model with the customer data, analyzing the neural network model produced by the training for an accuracy, and improving the accuracy by iteratively repeating the training of the neural network model until a customer-defined constraint is met, as determined by the first algorithm.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory comprising instructions, which are executed by the at least one processor and configure the processor to implement a patient information extractor. The patient information extractor receives a query specification for executing a query on a patient electronic medical record (EMR). The query specification provides parameters indicating a methodology for extracting search results from the patient EMR. The patient information extractor retrieves the patient EMR from a patient registry. The patient information extractor automatically executes the query specification on the retrieved patient EMR to thereby extract the search results from the patient EMR in accordance with the parameters of the query specification. The patient information extractor automatically processes the extracted search results to generate a patient indicator value.