Abstract: Methods and systems for obtaining contextual information about a machine learning model are provided. The method includes: receiving raw data that is usable for training a model; training the by using the raw data; computing a set of common background data based on the raw data; computing a first explanation based on an output of the model and the set of common background data; computing, based on an output of the model, an agnostic model representation of the model; computing, based on the first explanation and the agnostic model representation, a deep, compact, and dense explanation-driven representation of the model; and determining, based on the explanation-driven representation, contextual information that relates to the model.

Abstract: A method for using a Gaussian Process-based algorithm to approximate an optimal stopping of a time series that corresponds to a sequence of events is provided. The method includes: receiving information that relates to an event sequence; estimating, based on the received information, a first potential reward that is obtained by stopping the event sequence at a first time, and a set of respective second potential rewards that are obtained by stopping the event sequence at corresponding times; and determining, based on the estimated first and second potential rewards, an optimal time for stopping the event sequence. The event sequence may include a numerical sequence that is modeled as a statistical learning method via a Gaussian Process (GP) function and/or a deep GP function that indicates a probability density distribution of the items in the numerical sequence over a predetermined time interval.

Abstract: A method and a system for performing stochastic sequential assignment of jobs with random arrival times is provided. The method includes receiving a first plurality of jobs in a sequence; and sequentially applying, to each respective job from among the first plurality of jobs, a non-parametric sequential allocation algorithm in order to determine whether to accept the respective job or to decline the respective job. The application of the non-parametric sequential allocation algorithm includes calculating, for each respective job, a corresponding reward value that relates to a reward that is gained when the respective job is accepted; and maximizing an expected cumulative reward value based on the calculated reward values.

Abstract: Various methods, apparatuses/systems, and media for computing change-agnostic data points are disclosed. A processor trains a machine learning model by using the at least the first set of raw data; computes a set of explanations for all combinations based on output data of the trained machine learning model, the first set of raw data, and sampled raw data computed by applying a sampling algorithm on the raw data; computes a compact representation of the set of explanations corresponding to a pre-configured dimension based on compression quality and generating a set of compressed explanations; computes a unique representation of model explanation with respect to the pre-configured dimension; determines whether the model explanation is robust to changes in data through data perturbation; and computes change-agnostic data points based on determining that the model explanation is robust to changes in data through data perturbation.

Abstract: Various methods, apparatuses/systems, and media for computing strategies for model inferences are disclosed. A processor generates background data from raw data and data sampling strategies associated with a particular security instrument; computes a model for each pair of raw data and machine learning algorithm; computes model explanation for each pair of the background data and the model; normalizes the computed model explanation by utilizing a predefined algorithm; computes a deep dense representation of each explanation based on the normalized explanation of the computed model; clusters the deep dense representation of each explanation; computes a deep dense representation of explanation of each predicted target data version associated with features recovery; compares the deep dense representation of each explanation with the deep dense representation of explanation of each predicted target data version; and computes a strategy of model selection for each target data version as output.

Abstract: A method for evaluating classification models that are trained by using incomplete data sets for which data is known to be missing and the missing data is known to be non-random is provided. The method includes: receiving first information that relates to data to be used for training and evaluating a performance of a classification model that is designed to make a determination with respect to a particular query; analyzing the first information to determine second information that relates to a known portion of the first information and third information that relates to missing data; estimating, based on the third information, an uncertainty that corresponds to the missing data; and calculating, based on the second information and the estimated uncertainty, a first Gaussian approximation to a performance metric that relates to the first classification model.

Abstract: A method for evaluating classification models that are trained by using incomplete data sets for which data is known to be missing and the missing data is known to be non-random is provided. The method includes: receiving first information that relates to data to be used for training and evaluating a performance of a classification model that is designed to make a determination with respect to a particular query; analyzing the first information to determine second information that relates to a known portion of the first information and third information that relates to missing data; estimating, based on the third information, an uncertainty that corresponds to the missing data; and calculating, based on the second information and the estimated uncertainty, a first Gaussian approximation to a performance metric that relates to the first classification model.

Abstract: A method for using graphical model tools to evaluate classification models that are trained by using incomplete data sets for which data is known to be missing and the missing data is known to be non-random is provided. The method includes: receiving first information that relates to first data to be used for training and evaluating a performance of a first classification model; analyzing the first information to determine second information that relates to missing data; generating a missingness graph that relates to a description of how the missing data has come to be missing; decomposing, based on the missingness graph, an expression that relates to a classification metric into recoverable terms and non-recoverable terms; training a second classification model to generate respective weights for the recoverable terms; and calculating, based on the non-recoverable terms, an upper bound and a lower bound on the classification metric.

Abstract: Methods and systems for obtaining contextual information about a machine learning model are provided. The method includes: receiving raw data that is usable for training a model; training the by using the raw data; computing a set of common background data based on the raw data; computing a first explanation based on an output of the model and the set of common background data; computing, based on an output of the model, an agnostic model representation of the model; computing, based on the first explanation and the agnostic model representation, a deep, compact, and dense explanation-driven representation of the model; and determining, based on the explanation-driven representation, contextual information that relates to the model.

Abstract: Various methods, apparatuses/systems, and media for computing change-agnostic data points are disclosed. A processor trains a machine learning model by using the at least the first set of raw data; computes a set of explanations for all combinations based on output data of the trained machine learning model, the first set of raw data, and sampled raw data computed by applying a sampling algorithm on the raw data; computes a compact representation of the set of explanations corresponding to a pre-configured dimension based on compression quality and generating a set of compressed explanations; computes a unique representation of model explanation with respect to the pre-configured dimension; determines whether the model explanation is robust to changes in data through data perturbation; and computes change-agnostic data points based on determining that the model explanation is robust to changes in data through data perturbation.

Abstract: Various methods, apparatuses/systems, and media for computing strategies for model inferences are disclosed. A processor generates background data from raw data and data sampling strategies associated with a particular security instrument; computes a model for each pair of raw data and machine learning algorithm; computes model explanation for each pair of the background data and the model; normalizes the computed model explanation by utilizing a predefined algorithm; computes a deep dense representation of each explanation based on the normalized explanation of the computed model; clusters the deep dense representation of each explanation; computes a deep dense representation of explanation of each predicted target data version associated with features recovery; compares the deep dense representation of each explanation with the deep dense representation of explanation of each predicted target data version; and computes a strategy of model selection for each target data version as output.

Abstract: A method for using a Gaussian Process-based algorithm to approximate an optimal stopping of a time series that corresponds to a sequence of events is provided. The method includes: receiving information that relates to an event sequence; estimating, based on the received information, a first potential reward that is obtained by stopping the event sequence at a first time, and a set of respective second potential rewards that are obtained by stopping the event sequence at corresponding times; and determining, based on the estimated first and second potential rewards, an optimal time for stopping the event sequence. The event sequence may include a numerical sequence that is modeled as a statistical learning method via a Gaussian Process (GP) function and/or a deep GP function that indicates a probability density distribution of the items in the numerical sequence over a predetermined time interval.

Abstract: A method and a system for generating a counterfactual explanation for an artificial intelligence (AI) regression model are provided. The method includes: obtaining a mathematical expression that corresponds to the AI regression model and a first value that corresponds to a query instance; and defining a counterfactual potential function by performing a differential continuous mapping between respective output values of the obtained mathematical expression and a real line over a predetermined subset of real numbers. The counterfactual explanation is generated based on a value of the obtained mathematical expression that corresponds to a maximum value of the determined at least one counterfactual potential function.

Abstract: A method and a system for performing stochastic sequential assignment of jobs with random arrival times is provided. The method includes receiving a first plurality of jobs in a sequence; and sequentially applying, to each respective job from among the first plurality of jobs, a non-parametric sequential allocation algorithm in order to determine whether to accept the respective job or to decline the respective job. The application of the non-parametric sequential allocation algorithm includes calculating, for each respective job, a corresponding reward value that relates to a reward that is gained when the respective job is accepted; and maximizing an expected cumulative reward value based on the calculated reward values.

Abstract: Systems and methods for multi-agent based fraud detection are disclosed. A method may include: providing a generator configuration file comprising a plurality of transaction behaviors and a number or proportion of generator agents to act in accordance with each transaction behavior; providing a detector configuration file comprising a detector parameter for a plurality of detector agents to use; generating a first set of test data using the generator agents based on the transaction behavior, wherein the first set of generated test data may include a first set of generated test transactions, and each generated test transactions may include a fraud indicator based on the transaction behavior; training a plurality of detector agents using the first set of generated test data and the detector configuration file, wherein each detector agent outputs a first trained model object; and outputting a first trained detection model based on the first trained model objects.

Abstract: Systems and methods for applying game theory for fraud detection. Rather than inspecting every transaction record, embodiments are directed to limiting incoming suspicious transaction records according to a schedule. The schedule may define time windows for various clients and transactions. These time windows may filter down the stream of incoming transaction records to a subset. As a result, fraud may be detected by strategically allocating resources in an optimal way rather than attempting to inspect each and every instance of transaction.