Invention Application
- Patent Title: DETERMINING A HYPERPARAMETER FOR INFLUENCING NON-LOCAL SAMPLES IN MACHINE LEARNING
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Application No.: US17366249Application Date: 2021-07-02
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Publication No.: US20230004860A1Publication Date: 2023-01-05
- Inventor: Donglin Hu , Yuxi Zhang , Kexin Xie
- Applicant: salesforce.com, inc.
- Applicant Address: US CA San Francisco
- Assignee: salesforce.com, inc.
- Current Assignee: salesforce.com, inc.
- Current Assignee Address: US CA San Francisco
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06N7/00

Abstract:
Methods, computer readable media, and devices for determining a hyperparameter for influencing non-local samples in machine learning are disclosed. One method may include identifying a set of local samples associated with a first entity, identifying a set of non-local samples comprising samples associated with a plurality of entities other than the first entity, assigning a local sample weight to one or more samples of the set of local samples, determining a range of non-local sample weights, determining a range of hyperparameters based on the range of non-local sample weights, determining an optimized hyperparameter based on the range of hyperparameters, assigning an optimized non-local sample weight to one or more samples of the set of non-local samples, and generating a prediction using machine learning.
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