公开(公告)号：US11429894B2

公开(公告)日：2022-08-30

申请号：US16288217

申请日：2019-02-28

Applicant: Google LLC

Inventor： Elad Edwin Tzvi Eban ,  Alan Mackey ,  Xiyang Luo

IPC: G06N20/00

Abstract: Example aspects of the present disclosure are directed to systems and methods for learning classification models which satisfy constraints such as, for example, constraints that can be expressed as a predicted positive rate or negative rate on a subset of the training dataset. In particular, through the use of quantile estimators, the systems and methods of the present disclosure can transform a constrained optimization problem into an unconstrained optimization problem that is solved more efficiently and generally than the constrained optimization problem. As one example, the unconstrained optimization problem can include optimizing an objective function where a decision threshold of the classification model is expressed as an estimator of a quantile function on the classification scores of the machine-learned classification model for a subset of the training dataset at a desired quantile.

公开(公告)号：US20240428137A1

公开(公告)日：2024-12-26

申请号：US18750814

申请日：2024-06-21

Applicant: Google LLC

Inventor： Elad Edwin Tzvi Eban ,  Alan Mackey ,  Piotr Zielinski

IPC: G06N20/00

Abstract: Systems and methods described herein can improve typicality of batches for machine learning. The systems and methods can include obtaining a corpus of training data, the corpus of training data including one or more training examples. The systems and methods can include generating a first batch set including a plurality of batches from the corpus of training data, each of the batches including a subset of the one or more training examples. The systems and methods can include determining a batch distribution of a first batch of the first batch set. The systems and methods can include determining that the first batch is an atypical batch based on the batch distribution of the first batch. The systems and methods can include, in response to determining that the first batch is an atypical batch, shuffling the training examples of the first batch and one or more second batches of the first batch set to generate a second batch set. The systems and methods can include training a first machine-learned model using the second batch set.

公开(公告)号：US20190266513A1

公开(公告)日：2019-08-29

申请号：US16288217

申请日：2019-02-28

Applicant: Google LLC

Inventor： Elad Edwin Tzvi Eban ,  Alan Mackey ,  Xiyang Luo

IPC: G06N20/00

Abstract: Example aspects of the present disclosure are directed to systems and methods for learning classification models which satisfy constraints such as, for example, constraints that can be expressed as a predicted positive rate or negative rate on a subset of the training dataset. In particular, through the use of quantile estimators, the systems and methods of the present disclosure can transform a constrained optimization problem into an unconstrained optimization problem that is solved more efficiently and generally than the constrained optimization problem. As one example, the unconstrained optimization problem can include optimizing an objective function where a decision threshold of the classification model is expressed as an estimator of a quantile function on the classification scores of the machine-learned classification model for a subset of the training dataset at a desired quantile.