SHARED LEARNING ACROSS SEPARATE ENTITIES WITH PRIVATE DATA FEATURES

    公开(公告)号:US20240303554A1

    公开(公告)日:2024-09-12

    申请号:US18664202

    申请日:2024-05-14

    Applicant: Stripe, Inc.

    CPC classification number: G06N20/20 G06F18/24323 G06N3/084 G06N5/043

    Abstract: Embodiments herein use transfer learning paradigms to facilitate classification across entities without requiring the entities access to the other party's sensitive data. In one or more embodiments, one entity may train a model using its own data (which may include at least some non-shared data) and shares either the scores (or an intermediate representation of the scores). One or more other parties may use the scores as a feature in its own model. The scores may be considered to act as an embedding of the features but do not reveal the features. In other embodiments, parties may be used to train part of a model or participate in generating one or more nodes of a decision tree without revealing all its features. The trained models or decision trees may then be used for classifying unlabeled events or items.

    SHARED LEARNING ACROSS SEPARATE ENTITIES WITH PRIVATE DATA FEATURES

    公开(公告)号:US20200242492A1

    公开(公告)日:2020-07-30

    申请号:US16258116

    申请日:2019-01-25

    Applicant: Stripe, Inc.

    Abstract: Embodiments herein use transfer learning paradigms to facilitate classification across entities without requiring the entities access to the other party's sensitive data. In one or more embodiments, one entity may train a model using its own data (which may include at least some non-shared data) and shares either the scores (or an intermediate representation of the scores). One or more other parties may use the scores as a feature in its own model. The scores may be considered to act as an embedding of the features but do not reveal the features. In other embodiments, parties may be used to train part of a model or participate in generating one or more nodes of a decision tree without revealing all its features. The trained models or decision trees may then be used for classifying unlabeled events or items.

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