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US-10992765-B2 · Apr 27, 2021 · US
Determining a hyperparameter for influencing non-local samples in machine learning
US2023004860A1 · US · A1
| Field | Value |
|---|---|
| Publication number | US-2023004860-A1 |
| Application number | US-202117366249-A |
| Country | US |
| Kind code | A1 |
| Filing date | Jul 2, 2021 |
| Priority date | Jul 2, 2021 |
| Publication date | Jan 5, 2023 |
| Grant date | — |
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Abstract
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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.
First claim
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What is claimed is: 1 . A computer-implemented for determining a hyperparameter for influencing non-local samples in machine learning, the method comprising: 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, the optimized non-local sample weight based on the optimized hyperparameter; and generating a prediction using machine learning, the prediction associated with the first entity and being based on: the set of local samples; the set of non-local samples; the local sample weight; and the optimized non-local sample weight. 2 . The computer-implemented method of claim 1 , wherein the local sample weight is 1. 3 . The computer-implemented method of claim 1 , wherein the range of non-local sample weights is between: a total number of samples in the set of local samples over a total number of samples in the set of local samples and the set of non-local samples; and the integer value 1. 4 . The computer-implemented method of claim 1 , wherein determining a range of hyperparameters based on the range of non-local sample weights comprises, for any one non-local sample weight, determining an associated hyperparameter to be a ratio of a total number of samples in the set of local samples to a difference between a total number of samples in the set of non-local samples and the total number of samples in the set of local samples multiplied by the one non-local sample weight plus the total number of samples in the set of local samples. 5 . The computer-implemented method of claim 1 , wherein determining a range of hyperparameters based on the range of non-local sample weights comprises, for any one non-local sample weight, determining an associated hyperparameter to be a ratio of a total number of samples in the set of local samples multiplied by the local sample weight to a difference between a total number of samples in the set of non-local samples and the total number of samples in the set of local samples multiplied by the one non-local sample weight plus the total number of samples in the set of local samples multiplied by the local sample weight. 6 . The computer-implemented method of claim 1 , wherein determining a range of hyperparameters based on the range of non-local sample weights comprises, for any one non-local sample weight, determining an associated hyperparameter to be a ratio of a sum of local sample weights assigned to the one or more samples of the set of local samples to a sum of non-local sample weights assigned to the one or more samples of the set of non-local samples plus the sum of local sample weights assigned to the one or more samples of the set of local samples. 7 . The computer-implemented method of claim 1 , wherein determining an optimized hyperparameter based on the range of hyperparameters comprises performing a grid search. 8 . The computer-implemented method of claim 1 , wherein determining an optimized hyperparameter based on the range of hyperparameters comprises utilizing a Bayesian optimization. 9 . The computer-implemented method of claim 1 , wherein the prediction is a prediction of an action to be taken by one or more individuals associated with the first entity. 10 . A non-transitory machine-readable storage medium that provides instructions that, if executed by a processor, are configurable to cause the processor to perform operations comprising: 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, the optimized non-local sample weight based on the optimized hyperparameter; and generating a prediction using machine learning, the prediction associated with the first entity and being based on: the set of local samples; the set of non-local samples; the local sample weight; and the optimized non-local sample weight. 11 . The non-transitory machine-readable storage medium of claim 10 , wherein the range of non-local sample weights is between: a total number of samples in the set of local samples over a total number of samples in the set of local samples and the set of non-local samples; and the integer value 1. 12 . The non-transitory machine-readable storage medium of claim 10 , wherein determining a range of hyperparameters based on the range of non-local sample weights comprises, for any one non-local sample weight, determining an associated hyperparameter to be a ratio of a total number of samples in the set of local samples to a difference between a total number of samples in the set of non-local samples and the total number of samples in the set of local samples multiplied by the one non-local sample weight plus the total number of samples in the set of local samples. 13 . The non-transitory machine-readable storage medium of claim 10 , wherein determining an optimized hyperparameter based on the range of hyperparameters comprises performing a grid search. 14 . The non-transitory machine-readable storage medium of claim 10 , wherein determining an optimized hyperparameter based on the range of hyperparameters comprises utilizing a Bayesian optimization. 15 . The non-transitory machine-readable storage medium of claim 10 , wherein the prediction is a prediction of an action to be taken by one or more individuals associated with the first entity. 16 . An apparatus comprising: a processor; and a non-transitory machine-readable storage medium that provides instructions that, if executed by a processor, are configurable to cause the processor to perform operations comprising: 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, the optimized non-local sample weight based on the optimized hyperparameter; and generating a prediction using machine learning, the prediction associated with the first entity and being based on: the set of local samples; the set of non-local samples; the local sample weight; and the optimized non-local sample weight. 17 . The apparatus of claim 16 , wherein the range of non-local sample weights is between: a total number of s
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- What does patent US2023004860A1 cover?
- 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 …
- Who is the assignee on this patent?
- Salesforce Com Inc
- What technology area does this patent fall under?
- Primary CPC classification G06N7/01. Mapped technology areas include Physics.
- When was this patent published?
- Publication date Thu Jan 05 2023 00:00:00 GMT+0000 (Coordinated Universal Time) (A1). Legal status and post-grant events are not shown on this page.
- What related patents are in patentsdb?
- We list 4 related publications on this page (citations in our corpus or others sharing the same primary CPC).