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    CONDITIONALLY INDEPENDENT DATA GENERATION FOR TRAINING MACHINE LEARNING SYSTEMS
    21.
    发明申请
    CONDITIONALLY INDEPENDENT DATA GENERATION FOR TRAINING MACHINE LEARNING SYSTEMS 有权

    公开(公告)号：US20230021338A1

    公开(公告)日：2023-01-26

    申请号：US17368925

    申请日：2021-07-07

    Applicant: International Business Machines Corporation

    Inventor： Kartik Ahuja ,  Prasanna Sattigeri ,  Karthikeyan Shanmugam ,  Dennis Wei ,  Murat Kocaoglu ,  Karthikeyan Natesan Ramamurthy

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A method for training a machine learning system using conditionally independent training data includes receiving an input dataset (p(x, y, z)). A generative adversarial network, that includes a generator and a first discriminator, uses the input dataset to generate a training data (ps (xf, yf, zf)) by generating the values (xf, yf, zf). The first discriminator determines a first loss (L1) based on (xf, yf, zf) and (x, y, z). A divergence calculator modifies the training data based on a dependence measure (γ). The divergence calculator includes a second discriminator and a third discriminator. Modifying the training data includes receiving a reference value ({tilde over (y)}), and computing, by the second discriminator, a second loss (L2) based on (xf, yf, zf) and (xf, {tilde over (y)}, zf). The third discriminator computes a third loss (L3) based on (yf, zf) and ({tilde over (y)}, zf). Further, a fourth loss (L4) is computed based on L2 and L3. The training data is output from the generator if L1 and L4 satisfy a predetermined condition.

    POST-HOC LOCAL EXPLANATIONS OF BLACK BOX SIMILARITY MODELS
    22.
    发明申请
    POST-HOC LOCAL EXPLANATIONS OF BLACK BOX SIMILARITY MODELS 有权

    公开(公告)号：US20220391631A1

    公开(公告)日：2022-12-08

    申请号：US17382310

    申请日：2021-07-21

    Applicant: International Business Machines Corporation ,  RENSSELAER POLYTECHNIC INSTITUTE

    Inventor： Zaid Bin Tariq ,  Karthikeyan Natesan Ramamurthy ,  Dennis Wei ,  Amit Dhurandhar

    IPC: G06K9/62 ,  G06N20/00

    Abstract: Define a similarity measure between first and second points in a data space by operation of a machine learning model. Generate interpretable representations of the first and second points. Generate an interpretable local description of the similarity measure by approximating the similarity measure as a distance between the interpretable representations of the first and second points. The distance between the interpretable representations incorporates a matrix. Learn values for the matrix through optimizing a loss function evaluated on perturbations of the first and second points. Explain a value of the similarity measure between the first and second points using elements of the matrix. Assess the explanation of the value of the similarity measure using a rubric. In response to the assessment of the explanation of the value of the similarity measure, modify the machine learning model. Deploy the modified machine learning model.

    METHOD FOR MARKET RISK ASSESSMENT FOR HEALTHCARE APPLICATIONS
    28.
    发明申请
    METHOD FOR MARKET RISK ASSESSMENT FOR HEALTHCARE APPLICATIONS 审中-公开
    用于医疗保健应用的市场风险评估方法

    公开(公告)号：US20160321748A1

    公开(公告)日：2016-11-03

    申请号：US14699482

    申请日：2015-04-29

    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

    Inventor： Shilpa Mahatma ,  Aleksandra Mojsilovic ,  Karthikeyan Natesan Ramamurthy ,  Kush R. Varshney ,  Dennis Wei ,  Gigi Yuen-Reed

    IPC: G06Q40/04 ,  G06F19/00

    CPC classification number: G06Q40/04 ,  G06F19/328

    Abstract: Exemplary embodiments of the present invention provide a method of health insurance market risk assessment including receiving first data including demographic and cost data for members of a health insurance plan in a current market, receiving second data including demographic data for the current market, and receiving third data including demographic data for a new market. The first to third data are used to transform a distribution of the plan members to account for differences between the current and new market demographic data and to estimate probabilities of enrollment in the new market. A statistical model is learned to predict risk in the new market using the transformed distribution and the estimated probabilities. The statistical model is used to determine risk of entering the new market.

    Abstract translation: 本发明的示例性实施例提供一种健康保险市场风险评估方法，包括接收包括当前市场中健康保险计划成员的人口和成本数据的第一数据，接收包括当前市场的人口统计数据的第二数据，以及接收第三 数据包括新市场的人口统计数据。 第一到第三个数据用于改变计划成员的分布，以解决当前和新的市场人口统计数据之间的差异，并估计新市场的入学概率。 使用统计模型学习使用转换分布和估计概率来预测新市场中的风险。 统计模型用于确定进入新市场的风险。

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