Abstract: Embodiments are disclosed for a method. The method includes receiving feedback decision rules for multiple predictions by a trained machine learning model. generating a feedback rule set based on the feedback decision rules. The method further includes generating an updated training dataset based on an original training dataset and an updated feedback rule set. The updated feedback rule set resolves one or more conflicts of the feedback rule set, and the updated training dataset is configured to train the machine learning model to move a decision boundary. Generating the updated training dataset includes generating multiple updated training instances by applying one of the feedback decision rules to a training instance of the original training dataset.

Abstract: An aerosol-generating device for generating aerosol from an aerosol-forming substrate is provided, the device including: a housing defining a cavity configured to at least partially receive the substrate; and a sensing assembly including an emitter configured to emit electromagnetic radiation into the cavity, a receiver configured to receive electromagnetic radiation from the cavity, the receiver including a sensor configured to measure at least one wavelength of the received radiation, and a shield extern to the cavity such that the receiver is between the shield and the cavity, the shield being configured to block electromagnetic radiation, first and second portions of the shield being planar, the first and the second portions being non-co-planar, an angle between a normal of respective planes of the first and the second portions being substantially the same as an angle between the receiver and the emitter, and the receiver and the emitter being non-parallel.

Abstract: Techniques regarding determining sufficiency of one or more machine learning models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in memory. The computer executable components can comprise a measurement component that measures maximum deviation of a supervised learning model from a reference model over a certification set and an analysis component that determines sufficiency of the supervised learning model based at least in part on the maximum deviation.

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: Obtain, from an existing machine learning classifier, original probabilistic scores classifying samples taken from two or more groups into two or more classes via supervised machine learning. Associate the original probabilistic scores with a plurality of original Lagrange multipliers. Adjust values of the plurality of original Lagrange multipliers via low-dimensional convex optimization to obtain updated Lagrange multipliers that satisfy fairness constraints as compared to the original Lagrange multipliers. Based on the updated Lagrange multipliers, closed-form transform the original probabilistic scores into transformed probabilistic scores that satisfy the fairness constraints while minimizing loss in utility. The fairness constraints are with respect to the two or more groups.

Abstract: A computer-implemented method of decision-making using selective labels, includes receiving a conditional success probability value of a feature associated with an entity. A confidence value of the received success probability value is received. A parameter value that is a trade-off between a short-term learning and a long-term utility is selected. A decision is rendered to accept or reject the feature associated with the entity according to a machine learning policy.

Abstract: A method for training a machine learning system using conditionally independent training data includes receiving an input dataset (p(x, y, z)). A generative adversarial network, that includes a generator and a first discriminator, uses the input dataset to generate a training data (ps (xf, yf, zf)) by generating the values (xf, yf, zf). The first discriminator determines a first loss (L1) based on (xf, yf, zf) and (x, y, z). A divergence calculator modifies the training data based on a dependence measure (?). The divergence calculator includes a second discriminator and a third discriminator. Modifying the training data includes receiving a reference value ({tilde over (y)}), and computing, by the second discriminator, a second loss (L2) based on (xf, yf, zf) and (xf, {tilde over (y)}, zf). The third discriminator computes a third loss (L3) based on (yf, zf) and ({tilde over (y)}, zf). Further, a fourth loss (L4) is computed based on L2 and L3.

Abstract: Define a similarity measure between first and second points in a data space by operation of a machine learning model. Generate interpretable representations of the first and second points. Generate an interpretable local description of the similarity measure by approximating the similarity measure as a distance between the interpretable representations of the first and second points. The distance between the interpretable representations incorporates a matrix. Learn values for the matrix through optimizing a loss function evaluated on perturbations of the first and second points. Explain a value of the similarity measure between the first and second points using elements of the matrix. Assess the explanation of the value of the similarity measure using a rubric. In response to the assessment of the explanation of the value of the similarity measure, modify the machine learning model. Deploy the modified machine learning model.

Abstract: Embodiments are disclosed for a method. The method includes receiving feedback decision rules for multiple predictions by a trained machine learning model. generating a feedback rule set based on the feedback decision rules. The method further includes generating an updated training dataset based on an original training dataset and an updated feedback rule set. The updated feedback rule set resolves one or more conflicts of the feedback rule set, and the updated training dataset is configured to train the machine learning model to move a decision boundary. Generating the updated training dataset includes generating multiple updated training instances by applying one of the feedback decision rules to a training instance of the original training dataset.

Abstract: In a method for interpreting output of a machine learning model, a processor receives a first interpretable rule set. A processor may also receive a second interpretable rule set generated from a dataset and model-predicted labels classifying the dataset. A processor may also generate a difference metric and mapping between the first interpretable rule set and the second interpretable rule set.

Abstract: A method of utilizing a computing device to correct source data used in machine learning includes receiving, by the computing device, first data. The computing device corrects the source data via an application of a covariate shift to the source data based upon the first data where the covariate shift re-weighs the source data.

Abstract: Obtain, from an existing machine learning classifier, original probabilistic scores classifying samples taken from two or more groups into two or more classes via supervised machine learning. Associate the original probabilistic scores with a plurality of original Lagrange multipliers. Adjust values of the plurality of original Lagrange multipliers via low-dimensional convex optimization to obtain updated Lagrange multipliers that satisfy fairness constraints as compared to the original Lagrange multipliers. Based on the updated Lagrange multipliers, closed-form transform the original probabilistic scores into transformed probabilistic scores that satisfy the fairness constraints while minimizing loss in utility. The fairness constraints are with respect to the two or more groups.

Abstract: Modularized data processing systems and methods for its use are provided. Processing a current job can reuse data generated for a previously processed job to the extent the two share parameter configurations. Similarly, outputs of processing modules generated during processing the previously processed job can be used as inputs to processing modules processing a current job, if the two jobs share some parameter configurations.

Abstract: A method of utilizing a computing device to correct source data used in machine learning includes receiving, by the computing device, first data. The computing device corrects the source data via an application of a covariate shift to the source data based upon the first data where the covariate shift re-weighs the source data.

Abstract: A method, computer system, and a computer program product for generating and reporting a plurality of health insurance cost predictions via private transfer learning is provided. The present invention may include retrieving a set of source data, and a set of target data. The present invention may then include creating and anonymizing a plurality of source data sets, and at least one target data set. The present invention may further include generating one or more source learner models, and a target learner model. The present invention may then include combining the one or more generated source learner models and the generated target learner model to generate a transfer learner. The present invention may further include generating a prediction based on the generated transfer learner.

Abstract: Modularized data processing systems and methods for its use are provided. Processing a current job can reuse data generated for a previously processed job to the extent the two share parameter configurations. Similarly, outputs of processing modules generated during processing the previously processed job can be used as inputs to processing modules processing a current job, if the two jobs share some parameter configurations.

Abstract: A system may predict costs for a set of members by building and using a predictive pipeline. The pipeline may be built using a set of historical data for training members. A set of member-level features can be identified by performing empirical testing on the set of historical data. The trained configurable predictive pipeline can generate a set of predictive data for each member, using historical test data for a set of testing members. The system can then generate a predictive report for each set of predictive data.

Abstract: A system, method and program product for cost attribution using multiple factors, in which transactional data sets from two or more time periods are analyzed based on multiple potential factors in the data sets that can be correlated to cost. The potential factors are systematically analyzed to identify a set of cost factors and compute the cost impact for each cost factor. An infrastructure is disclosed having a data selection system; a potential factors system; a factor hierarchy system; an actionability class system; a factor processing system and a cost factor reporting system for providing the cost impact of the set of cost factors based on analysis of the transactional data sets.

Abstract: Exemplary embodiments of the present invention provide a method of health insurance market risk assessment including receiving first data including demographic and cost data for members of a health insurance plan in a current market, receiving second data including demographic data for the current market, and receiving third data including demographic data for a new market. The first to third data are used to transform a distribution of the plan members to account for differences between the current and new market demographic data and to estimate probabilities of enrollment in the new market. A statistical model is learned to predict risk in the new market using the transformed distribution and the estimated probabilities. The statistical model is used to determine risk of entering the new market.

Abstract: A saturated input signal acquired by a synthetic aperture radar (SAR) system is processed by estimating a reconstruction that generated the input signal, reproducing an input signal from an estimated reconstruction to generate a reproduced signal, comparing the reproduced signal with the input signal; adjusting an estimated reconstruction based on the comparison; and iterating from the reproducing step until a termination condition is reached.