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公开(公告)号：US20220405529A1
公开(公告)日：2022-12-22
申请号：US17345730
申请日：2021-06-11
摘要： The present invention provides techniques for learning Mahalanobis distance similarity metrics from data for individually fair machine learning models. In one aspect, a method for learning a fair Mahalanobis distance similarity metric includes: obtaining data with similarity annotations; selecting, based on the data obtained, a model for learning a Mahalanobis covariance matrix Σ; and learning the Mahalanobis covariance matrix Σ from the data using the model selected, wherein the Mahalanobis covariance matrix Σ fully defines the fair Mahalanobis distance similarity metric.
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公开(公告)号：US20210065897A1
公开(公告)日：2021-03-04
申请号：US16554344
申请日：2019-08-28
摘要： A feature vector characterizing a system to be analyzed via online partially rewarded machine learning is obtained. Based on the feature vector, a decision is made, via the machine learning, using an online policy. The system is observed for environmental feedback. In at least a first instance, wherein the observing indicates that the environmental feedback is available, the environmental feedback is obtained. In at least a second instance, wherein the observing indicates that the environmental feedback is missing, the environmental feedback is imputed via an online imputation method. the online policy is updated based on results of the obtained environmental feedback and the online imputation method. A decision is output based on the updated online policy.
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公开(公告)号：US11645097B2
公开(公告)日：2023-05-09
申请号：US17183744
申请日：2021-02-24
IPC分类号： G06F9/455 , G06F9/445 , G06F40/274
CPC分类号： G06F9/45512 , G06F9/44526 , G06F40/274
摘要： The present disclosure describes systems and methods for a command line interface with artificial intelligence integration. Embodiments of the disclosure provide a command line orchestration component (e.g., including a reinforcement learning model) that provides a generic command line interface environment (e.g., that researchers can interface using a simple sense-act application programming interface (API)). For instance, a command line orchestration component receives commands (e.g., text input) from a user via a command line interface, and the command line orchestration component can identify command line plugins and candidate response from the command line plugins. Further, the command line orchestration component may select a response from the candidate responses based on user preferences, user characteristics, etc., thus providing a generic command line interface environment for various users (e.g., including artificial intelligence developers and researchers).
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4.发明申请公开(公告)号：US20220245502A1
公开(公告)日：2022-08-04
申请号：US17161663
申请日：2021-01-29
发明人： Vatche Isahagian , Vinod Muthusamy , Yara Rizk , Sohini Upadhyay
摘要： A computer-implemented method, a computer program product, and a computer system for explaining black-box machine learning models. A computer or server determines a process-aware neighborhood around a data sample, using one or more business process rules. The computer or server computes proximity between the process-aware neighborhood and the data sample, using a process-aware distance metric. The computer or server finds, from a family of linear functions, a local linear model, by minimizing losses of respective ones of the linear functions and a black-box machine learning model. The computer or server provides the local linear model for explanation of output of the black-box model on the data sample.
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公开(公告)号：US11508480B2
公开(公告)日：2022-11-22
申请号：US16554344
申请日：2019-08-28
摘要： A feature vector characterizing a system to be analyzed via online partially rewarded machine learning is obtained. Based on the feature vector, a decision is made, via the machine learning, using an online policy. The system is observed for environmental feedback. In at least a first instance, wherein the observing indicates that the environmental feedback is available, the environmental feedback is obtained. In at least a second instance, wherein the observing indicates that the environmental feedback is missing, the environmental feedback is imputed via an online imputation method. the online policy is updated based on results of the obtained environmental feedback and the online imputation method. A decision is output based on the updated online policy.
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6.发明申请公开(公告)号：US20220318639A1
公开(公告)日：2022-10-06
申请号：US17213167
申请日：2021-03-25
发明人： Sohini Upadhyay , Mikhail Yurochkin , Debarghya Mukherjee , Yuekai Sun , Amanda Ruth Garcia Bower , Seyed Hamid Eftekhari , Alexander Vargo , Fan Zhang
摘要： Obtain a first data set, a second data set, and a machine learning model. Construct a sensitive subspace of the first data set that defines a fair metric for distance among elements of the first data set. Fairly train the machine learning model on the first data set using a distributionally robust optimization approach based on the fair metric. Produce an individually fair set of labels by applying the fairly trained machine learning model to the second data set. Allocate a resource according to the individually fair set of labels.
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公开(公告)号：US20220269519A1
公开(公告)日：2022-08-25
申请号：US17183744
申请日：2021-02-24
摘要： The present disclosure describes systems and methods for a command line interface with artificial intelligence integration. Embodiments of the disclosure provide a command line orchestration component (e.g., including a reinforcement learning model) that provides a generic command line interface environment (e.g., that researchers can interface using a simple sense-act application programming interface (API)). For instance, a command line orchestration component receives commands (e.g., text input) from a user via a command line interface, and the command line orchestration component can identify command line plugins and candidate response from the command line plugins. Further, the command line orchestration component may select a response from the candidate responses based on user preferences, user characteristics, etc., thus providing a generic command line interface environment for various users (e.g., including artificial intelligence developers and researchers).
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