公开(公告)号：US20250077934A1

公开(公告)日：2025-03-06

申请号：US17927105

申请日：2022-09-23

Applicant: Google LLC

Inventor： Gil Shamir ,  Zhuoshu Li

IPC: G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium for training and using distilled machine learning models. In one aspect, a method includes obtaining a first input that includes training example sets that each include one or more feature values and, for each item, an outcome label that represents whether the item had a positive outcome. A first machine learning model is trained using the first input and is configured to generate a set of scores that represents whether the item will have a positive outcome when presented in the context of the training example set and with each other item in the example set. A distilled machine learning model is trained using the set of scores for each example set. The distilled machine learning model is configured to generate a distilled score.

公开(公告)号：US20240242106A1

公开(公告)日：2024-07-18

申请号：US17927398

申请日：2022-09-23

Applicant: Google LLC

Inventor： Gil Shamir ,  Zhuoshu Li

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium for training and using machine learning (ML) models. In one aspect, a method includes receiving a digital component request. A first ML model can output scores indicating a likelihood of a positive outcome for digital components. Input data can be provided to a second ML model and can include feature values for a subset of digital components that were selected based on the output scores. The second ML model can be trained to output an engagement predictions and/or ranking of digital components based at least in part on feature values of digital components that will be provided together as recommendations, and can produce a second output that includes ranking and engagement predictions of the digital components in the subset of digital components. At least one digital component can be provided based on the second output.

公开(公告)号：US20230252281A1

公开(公告)日：2023-08-10

申请号：US17830561

申请日：2022-06-02

Applicant: Google LLC

Inventor： Gil Shamir ,  Zhuoshu Li

IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62

CPC classification number: G06N3/08 ,  G06N3/0454 ,  G06K9/6265 ,  G06K9/6298

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that obtain a first machine learning model that is configured to output a score. The training examples can each include feature values that represent features of an item, and an outcome label for the item. From the training examples, training pairs of training examples are determined. For each training pair: (i) a score is generated for each training example in the training pair using the first machine learning model; and (ii) for the training pair, a score difference of the scores generated for the training examples in the training pair is determined. Using the training pairs and the score differences, a second machine learning model is trained to produce score differences that, for the same training examples, are within a threshold value of the score differences produced by the first machine learning model.