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公开(公告)号：US11531845B1
公开(公告)日：2022-12-20
申请号：US17837444
申请日：2022-06-10
Applicant: SAS Institute Inc.
Inventor： Xin Jiang Hunt , Xinmin Wu , Ralph Walter Abbey
Abstract: A computing device trains a fair machine learning model. A prediction model is trained to predict a target value. For a number of iterations, a weight vector is computed using the bound value based on fairness constraints defined for a fairness measure type; a weight value is assigned to each observation vector based on the target value and a sensitive attribute value; the prediction model is trained with each weighted observation vector to predict the target value; and a conditional moments vector is computed based on the fairness constraints and the target and sensitive attribute values. Conditional moments difference values are computed. When the conditional moments difference values indicate to adjust the bound value, the bound value is updated and the process is repeated with the bound value replaced with the updated bound value until the conditional moments difference values indicate no further adjustment of the bound value is needed.
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公开(公告)号：US11416712B1
公开(公告)日：2022-08-16
申请号：US17560474
申请日：2021-12-23
Applicant: SAS Institute Inc.
Inventor： Amirhassan Fallah Dizche , Ye Liu , Xin Jiang Hunt , Jorge Manuel Gomes da Silva
Abstract: A computing device generates synthetic tabular data. Until a convergence parameter value indicates that training of an attention generator model is complete, conditional vectors are defined; latent vectors are generated using a predefined noise distribution function; a forward propagation of an attention generator model that includes an attention model integrated with a conditional generator model is executed to generate output vectors; transformed observation vectors are selected; a forward propagation of a discriminator model is executed with the transformed observation vectors, the conditional vectors, and the output vectors to predict whether each transformed observation vector and each output vector is real or fake; a discriminator model loss value is computed based on the predictions; the discriminator model is updated using the discriminator model loss value; an attention generator model loss value is computed based on the predictions; and the attention generator model is updated using the attention generator model loss value.
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公开(公告)号：US10699207B2
公开(公告)日：2020-06-30
申请号：US16597334
申请日：2019-10-09
Applicant: SAS Institute Inc.
Inventor： Xin Jiang Hunt , Jorge Manuel Gomes da Silva , Ilknur Kaynar Kabul
Abstract: A computing device computes a weight matrix to compute a predicted value. For each of a plurality of related tasks, an augmented observation matrix, a plug-in autocovariance matrix, and a plug-in covariance vector are computed. A weight matrix used to predict the characteristic for each of a plurality of variables and each of a plurality of related tasks is computed. (a) and (b) are repeated with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied: (a) a gradient descent matrix is computed using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, and (b) an updated weight matrix is computed using the computed gradient descent matrix.
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公开(公告)号：US20190303786A1
公开(公告)日：2019-10-03
申请号：US16445593
申请日：2019-06-19
Applicant: SAS Institute Inc.
Inventor： Xin Jiang Hunt , Saba Emrani , Jorge Manuel Gomes da Silva , Ilknur Kaynar Kabul
Abstract: A computing device computes a weight matrix to compute a predicted value. For each of a plurality of related tasks, an augmented observation matrix, a plug-in autocovariance matrix, and a plug-in covariance vector are computed. A weight matrix used to predict the characteristic for each of a plurality of variables and each of a plurality of related tasks is computed. (a) and (b) are repeated with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied: (a) a gradient descent matrix is computed using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, and (b) an updated weight matrix is computed using the computed gradient descent matrix.
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