Patents by Inventor Xin Jiang Hunt

Xin Jiang Hunt has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11531845
    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.
    Type: Grant
    Filed: June 10, 2022
    Date of Patent: December 20, 2022
    Assignee: SAS Institute Inc.
    Inventors: Xin Jiang Hunt, Xinmin Wu, Ralph Walter Abbey
  • Patent number: 11436444
    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.
    Type: Grant
    Filed: December 21, 2021
    Date of Patent: September 6, 2022
    Assignee: SAS Institute Inc.
    Inventors: Xinmin Wu, Xin Jiang Hunt
  • Patent number: 11416712
    Abstract: A computing device generates synthetic tabular data.
    Type: Grant
    Filed: December 23, 2021
    Date of Patent: August 16, 2022
    Assignee: SAS Institute, Inc.
    Inventors: Amirhassan Fallah Dizche, Ye Liu, Xin Jiang Hunt, Jorge Manuel Gomes da Silva
  • Patent number: 10699207
    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.
    Type: Grant
    Filed: October 9, 2019
    Date of Patent: June 30, 2020
    Assignee: SAS Institute Inc.
    Inventors: Xin Jiang Hunt, Jorge Manuel Gomes da Silva, Ilknur Kaynar Kabul
  • Publication number: 20200042893
    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.
    Type: Application
    Filed: October 9, 2019
    Publication date: February 6, 2020
    Inventors: Xin Jiang Hunt, Jorge Manuel Gomes da Silva, Ilknur Kaynar Kabul
  • Patent number: 10510022
    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.
    Type: Grant
    Filed: June 25, 2019
    Date of Patent: December 17, 2019
    Assignee: SAS INSTITUTE INC.
    Inventors: Ricky Dee Tharrington, Jr., Xin Jiang Hunt, Ralph Walter Abbey
  • Patent number: 10474959
    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.
    Type: Grant
    Filed: June 19, 2019
    Date of Patent: November 12, 2019
    Assignee: SAS Institute Inc.
    Inventors: Xin Jiang Hunt, Saba Emrani, Jorge Manuel Gomes da Silva, Ilknur Kaynar Kabul
  • Publication number: 20190303786
    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.
    Type: Application
    Filed: June 19, 2019
    Publication date: October 3, 2019
    Inventors: Xin Jiang Hunt, Saba Emrani, Jorge Manuel Gomes da Silva, Ilknur Kaynar Kabul
  • Patent number: 10402741
    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.
    Type: Grant
    Filed: December 6, 2017
    Date of Patent: September 3, 2019
    Assignee: SAS INSTITUTE INC.
    Inventors: Xin Jiang Hunt, Saba Emrani, Jorge Manuel Gomes da Silva, Ilknur Kaynar Kabul
  • Publication number: 20180336484
    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.
    Type: Application
    Filed: December 6, 2017
    Publication date: November 22, 2018
    Inventors: Xin Jiang Hunt, Saba Emrani, Jorge Manuel Gomes da Silva, Ilknur Kaynar Kabul