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公开(公告)号：US10402741B2
公开(公告)日：2019-09-03
申请号：US15833641
申请日：2017-12-06
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 predict a value for a characteristic in a scoring dataset. 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：US11436444B1
公开(公告)日：2022-09-06
申请号：US17557298
申请日：2021-12-21
Applicant: SAS Institute Inc.
Inventor： Xinmin Wu , Xin Jiang Hunt
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：US10510022B1
公开(公告)日：2019-12-17
申请号：US16451228
申请日：2019-06-25
Applicant: SAS Institute Inc.
Inventor： Ricky Dee Tharrington, Jr. , Xin Jiang Hunt , Ralph Walter Abbey
IPC: G06N20/00 , G06N5/04 , G06F17/16 , G06F16/245
Abstract: Systems and methods for machine learning, models, and related explainability and interpretability are provided. A computing device determines a contribution of a feature to a predicted value. A feature computation dataset is defined based on a selected next selection vector. A prediction value is computed for each observation vector included in the feature computation dataset using a trained predictive model. An expected value is computed for the selected next selection vector based on the prediction values. The feature computation dataset is at least a partial copy of a training dataset with each variable value replaced in each observation vector included in the feature computation dataset based on the selected next selection vector. Each replaced variable value is replaced with a value included in a predefined query for a respective variable. A Shapley estimate value is computed for each variable.
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公开(公告)号：US12093826B2
公开(公告)日：2024-09-17
申请号：US18444906
申请日：2024-02-19
Applicant: SAS Institute Inc.
Inventor： Xinmin Wu , Ricky Dee Tharrington, Jr. , Ralph Walter Abbey , Xin Jiang Hunt
Abstract: A computing device trains a fair prediction model while defining an optimal event cutoff value. (A) A prediction model is trained with observation vectors. (B) The prediction model is executed to define a predicted target variable value and a probability associated with an accuracy of the predicted target variable value. (C) A conditional moments matrix is computed based on fairness constraints, the predicted target variable value, and the sensitive attribute variable value of each observation vector. The predicted target variable value has a predefined target event value only when the probability is greater than a predefined event cutoff value. (D) (A) through (C) are repeated. (E) An updated value is computed for the predefined event cutoff value. (F) (A) through (E) are repeated. An optimal event cutoff value is defined from the predefined event cutoff values used when repeating (A) through (E). The optimal value and prediction model are output.
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公开(公告)号：US10474959B2
公开(公告)日：2019-11-12
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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Patent Agency Ranking
公开(公告)号：US20180336484A1
公开(公告)日：2018-11-22
申请号：US15833641
申请日：2017-12-06
Applicant: SAS Institute Inc.
Inventor： Xin Jiang Hunt , Saba Emrani , Jorge Manuel Gomes da Silva , Ilknur Kaynar Kabul
CPC classification number: G06N7/005 , G06F17/16 , G06F17/18 , G06N5/003 , G06N20/00 , G16H50/20 , G16H50/70
Abstract: A computing device computes a weight matrix to predict a value for a characteristic in a scoring dataset. 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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7.发明公开公开(公告)号：US20240193416A1
公开(公告)日：2024-06-13
申请号：US18444906
申请日：2024-02-19
Applicant: SAS Institute Inc.
Inventor： Xinmin Wu , Ricky Dee Tharrington, JR. , Ralph Walter Abbey , Xin Jiang Hunt
IPC: G06N5/022
CPC classification number: G06N5/022
Abstract: A computing device trains a fair prediction model while defining an optimal event cutoff value. (A) A prediction model is trained with observation vectors. (B) The prediction model is executed to define a predicted target variable value and a probability associated with an accuracy of the predicted target variable value. (C) A conditional moments matrix is computed based on fairness constraints, the predicted target variable value, and the sensitive attribute variable value of each observation vector. The predicted target variable value has a predefined target event value only when the probability is greater than a predefined event cutoff value. (D) (A) through (C) are repeated. (E) An updated value is computed for the predefined event cutoff value. (F) (A) through (E) are repeated. An optimal event cutoff value is defined from the predefined event cutoff values used when repeating (A) through (E). The optimal value and prediction model are output.
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公开(公告)号：US11790036B2
公开(公告)日：2023-10-17
申请号：US18051906
申请日：2022-11-02
Applicant: SAS Institute Inc.
Inventor： Xinmin Wu , Xin Jiang Hunt , Ralph Walter Abbey
Abstract: A computing device trains a fair machine learning model. A predicted target variable is defined using a trained prediction model. The prediction model is trained with weighted observation vectors. The predicted target variable is updated using the prediction model trained with weighted observation vectors. A true conditional moments matrix and a false conditional moments matrix are computed. The training and updating with weighted observation vectors are repeated until a number of iterations is performed. When a computed conditional moments matrix indicates to adjust a bound value, the bound value is updated based on an upper bound value or a lower bound value, and the repeated training and updating with weighted observation vectors is repeated with the bound value replaced with the updated bound value until the conditional moments matrix indicates no further adjustment of the bound value is needed. A fair prediction model is trained with the updated bound value.
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公开(公告)号：US20230205839A1
公开(公告)日：2023-06-29
申请号：US18051906
申请日：2022-11-02
Applicant: SAS Institute Inc.
Inventor： Xinmin Wu , Xin Jiang Hunt , Ralph Walter Abbey
Abstract: A computing device trains a fair machine learning model. A predicted target variable is defined using a trained prediction model. The prediction model is trained with weighted observation vectors. The predicted target variable is updated using the prediction model trained with weighted observation vectors. A true conditional moments matrix and a false conditional moments matrix are computed. The training and updating with weighted observation vectors are repeated until a number of iterations is performed. When a computed conditional moments matrix indicates to adjust a bound value, the bound value is updated based on an upper bound value or a lower bound value, and the repeated training and updating with weighted observation vectors is repeated with the bound value replaced with the updated bound value until the conditional moments matrix indicates no further adjustment of the bound value is needed. A fair prediction model is trained with the updated bound value.
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公开(公告)号：US20200042893A1
公开(公告)日：2020-02-06
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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