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    FAIRNESS FEATURE IMPORTANCE: UNDERSTANDING AND MITIGATING UNJUSTIFIABLE BIAS IN MACHINE LEARNING MODELS
    2.
    发明申请
    FAIRNESS FEATURE IMPORTANCE: UNDERSTANDING AND MITIGATING UNJUSTIFIABLE BIAS IN MACHINE LEARNING MODELS 有权

    公开(公告)号：US20250094862A1

    公开(公告)日：2025-03-20

    申请号：US18529182

    申请日：2023-12-05

    Applicant: Oracle International Corporation

    Inventor： Yasha Pushak ,  Mathieu Godbout ,  Hesam Fathi Moghadam ,  Sungpack Hong ,  Hassan Chafi

    IPC: G06N20/00

    Abstract: In an embodiment, a computer generates a respective original inference from each of many records. Permuted values are selected for a feature from original values of the feature. Based on the permuted values for the feature, a permuted inference is generated from each record. Fairness and accuracy of the original and permuted inferences are measured. For each of many features, the computer measures a respective impact on fairness of a machine learning model, and a respective impact on accuracy of the machine learning model. A global explanation of the machine learning model is generated and presented based on, for multiple features, the impacts on fairness and accuracy. Based on the global explanation, an interactive indication to exclude or include a particular feature is received. The machine learning model is (re-)trained based on the interactive indication to exclude or include the particular feature, which may increase the fairness of the model.

    Scouting queries for improving query planning in distributed asynchronous graph queries
    3.
    发明授权
    Scouting queries for improving query planning in distributed asynchronous graph queries 有权

    公开(公告)号：US12174831B2

    公开(公告)日：2024-12-24

    申请号：US18073629

    申请日：2022-12-02

    Applicant: Oracle International Corporation

    Inventor： Tomáš Faltín ,  Vasileios Trigonakis ,  Sungpack Hong ,  Hassan Chafi

    IPC: G06F16/245 ,  G06F16/2453 ,  G06F16/901

    Abstract: A graph processing system is provided for executing scouting queries for improving query planning. A query planner creates a plurality of scouting queries, each scouting query in the plurality of scouting queries corresponding to a query plan for a graph query and having an associated confidence value. A graph processing system performs limited execution of the plurality of scouting queries and determines a metric value for each scouting query in the plurality of scouting queries based on execution of the scouting query. The system determines a score for each scouting query in the plurality of scouting queries based on its metric value and the confidence value of the corresponding query plan and selects a query plan based on the scores of the plurality of scouting queries. The system executes the graph query based on the selected query plan.

    Multi-stage pipelining for distributed graph processing
    9.
    发明授权
    Multi-stage pipelining for distributed graph processing 有权

    公开(公告)号：US11363093B2

    公开(公告)日：2022-06-14

    申请号：US15968637

    申请日：2018-05-01

    Applicant: Oracle International Corporation

    Inventor： Jinsu Lee ,  Thomas Manhardt ,  Sungpack Hong ,  Petr Koupy ,  Hassan Chafi ,  Vasileios Trigonakis

    IPC: H04L29/08 ,  G06F16/901 ,  H04L67/10

    Abstract: Techniques are described herein for evaluating graph processing tasks using a multi-stage pipelining communication mechanism. In a multi-node system comprising a plurality of nodes, each node of said plurality of nodes executes a respective communication agent object. The respective communication agent object comprises: a sender lambda function is configured to perform sending operations and generate source messages based on the sender operations. An intermediate lambda function is configured to read source messages marked for a node, perform intermediate operations based on the source messages and generate intermediate messages based on the intermediate operations. A final receiver lambda function configured to: read intermediate messages marked for said each node, perform final operations based on the intermediate messages and generate a final result based on the final operations.

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