Abstract: A computer-implemented method, a computer program product, and a computer system for assessing fairness of a deep generative model. A computer system receives a user defined fairness criterion for the deep generative model. A computer system probes the deep generative model to produce samples for a target output. A computer system evaluates the samples for the fairness of the deep generative model, according to the user defined fairness criterion. A computer system produces a set of recommendations for modifying the deep generative model to meet the user defined fairness criterion, in response to determining that the deep generative model does not meet the user defined fairness criterion. In response to determining that the deep generative model is to be modified, a computer system applies at least one subset of the recommendations to the deep generative model. A computer system updates the deep generative model.

Abstract: Embodiments are provided for identification of a section of bodily tissue as either a candidate or a non-candidate for pathology tests. In some embodiments, a system can include a processor that executes computer-executable components stored in memory. The computer-executable components can include a feature composition component that generates a feature vector representing a physical model describing dye dynamics that determines a group of multispectral images of a section of bodily tissue. The computer-executable components also can include a classification component that generates a classification attribute for the section of bodily tissue by applying a classification model to the feature vector. The classification attribute designates the section of bodily tissue as one of biopsy-candidate or non-biopsy-candidate.

Abstract: A system, computer program product, and method are presented for administering examinations with adversarial hardening of queries against automated responses. The method include receiving an original query electronically. A response to the original query is to be submitted electronically by a human. The method also includes modifying the original query, thereby generating a modified query. The modified query is configured to be comprehensible by the human, and not properly responded to through electronic means without human support.

Abstract: The illustrative embodiments provide for supervision and reaction-aware adaptive intervention in an area. An embodiment includes detecting a behavior of a non-compliant entity over a threshold in a supervised area using one or more sensors. The threshold is determined by processing an input of the sensor using a first processing algorithm. The embodiment includes deploying a response into the supervised area. The response is based on input from the sensor. The embodiment includes identifying, using a second processing algorithm, a reaction of the non-compliant entity to the initial response. The embodiment includes determining fulfilment of a target state of the non-compliant entity using a third algorithm. The target state may include a change in behavior of the non-compliant entity. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the embodiment.

Abstract: Embodiments receive a matrix of a plurality of observations and a plurality of features; Perform feature selection of the matrix of the plurality of observations and the plurality of features by solving an optimizing function; and output a class probability prediction based on estimated coefficient parameter values for selected features based on performing the feature selection of the matrix. In further embodiments, the plurality of features are mixed and include categorical features, functional features, and continuous features.

Abstract: Techniques that facilitate three-dimensional (3D) delineation of tumor boundaries via one or more supervised machine learning algorithms are provided. An example embodiment includes a computer-implemented method that includes: extracting, by a computing system operatively coupled to a processor, one or more feature vectors from a time-series evolution of fluorescence distribution observed at a section of bodily tissue of interest, wherein the one or more feature vectors represent a physical model describing on-tissue dye dynamics of the section of bodily tissue; and generating, by the computing system, a classification attribute for the section of bodily tissue represented by the one or more feature vectors, wherein a pre-trained classifier designates the section of bodily tissue as a biopsy or a non-biopsy candidate through execution of the one or more supervised machine learning algorithms.

Abstract: A computer-implemented method, a computer program product, and a computer system for assessing fairness of a deep generative model. A computer system receives a user defined fairness criterion for the deep generative model. A computer system probes the deep generative model to produce samples for a target output. A computer system evaluates the samples for the fairness of the deep generative model, according to the user defined fairness criterion. A computer system produces a set of recommendations for modifying the deep generative model to meet the user defined fairness criterion, in response to determining that the deep generative model does not meet the user defined fairness criterion. In response to determining that the deep generative model is to be modified, a computer system applies at least one subset of the recommendations to the deep generative model. A computer system updates the deep generative model.

Abstract: Techniques that facilitate three-dimensional (3D) delineation of tumor boundaries via one or more supervised machine learning algorithms are provided. An example embodiment includes a computer-implemented method that includes: extracting, by a computing system operatively coupled to a processor, one or more feature vectors from a time-series evolution of fluorescence distribution observed at a section of bodily tissue of interest, wherein the one or more feature vectors represent a physical model describing on-tissue dye dynamics of the section of bodily tissue; and generating, by the computing system, a classification attribute for the section of bodily tissue represented by the one or more feature vectors, wherein a pre-trained classifier designates the section of bodily tissue as a biopsy or a non-biopsy candidate through execution of the one or more supervised machine learning algorithms.

Abstract: A system, computer program product, and method are presented for administering examinations with adversarial hardening of queries against automated responses. The method include receiving an original query electronically. A response to the original query is to be submitted electronically by a human. The method also includes modifying the original query, thereby generating a modified query. The modified query is configured to be comprehensible by the human, and not properly responded to through electronic means without human support.

Abstract: Systems and techniques that facilitate variable structure reinforcement learning are provided. In various embodiments, a system can comprise a data component that can access state information of a machine learning environment. In various instances, the system can further comprise a selection component that can select a reinforcement learning model from a set of available reinforcement learning models based on the state information. In various embodiments, the system can further comprise a model library component, which can respectively correlate the set of available reinforcement learning models with a set of environment assumptions. In various embodiments, the selection component can perform a statistical hypothesis test based on the state information. In various aspects, the selection component can identify an environment assumption in the set of environment assumptions that is consistent with results of the statistical hypothesis test.

Abstract: Embodiments are provided for identification of a section of bodily tissue as either a candidate or a non-candidate for pathology tests. In some embodiments, a system can include a processor that executes computer-executable components stored in memory. The computer-executable components can include a feature composition component that generates a feature vector representing a physical model describing dye dynamics that determines a group of multispectral images of a section of bodily tissue. The computer-executable components also can include a classification component that generates a classification attribute for the section of bodily tissue by applying a classification model to the feature vector. The classification attribute designates the section of bodily tissue as one of biopsy-candidate or non-biopsy-candidate.