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公开(公告)号：US20200097853A1
公开(公告)日：2020-03-26
申请号：US16615309
申请日：2017-06-02
Applicant: Google LLC
Inventor： Daniel Reuben Golovin , Gregory Peter Kochanski , John Karro
Abstract: A computer-implemented method for black box optimization of parameters of a system, product, or process, can include performing, by one or more computing devices, one or more iterations of a sequence of operations which includes: determining whether to sample an argument value from a feasible set of argument values using a first approach or using a second approach, wherein each argument value of the feasible set defines values for each of plural parameters of a system, product, or process; and based on the determination, sampling the argument value using the first approach or the second approach wherein the first approach includes sampling the argument value at random from the feasible set and the second approach includes sampling, by the one or more computing devices, the argument value from a subset of the feasible set that is defined based on a ball around a current best argument value.
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Patent Agency Ranking
公开(公告)号：US20230350775A1
公开(公告)日：2023-11-02
申请号：US18347386
申请日：2023-07-05
Applicant: Google LLC
Inventor： Daniel Reuben Golovin , Benjamin Solnik , Subhodeep Moitra , David W. Sculley, II
CPC classification number: G06F11/3409 , G06N20/00 , G06F11/3006 , G06N5/01 , G06N7/01
Abstract: The present disclosure provides computing systems and associated methods for optimizing one or more adjustable parameters (e.g. operating parameters) of a system. In particular, the present disclosure provides a parameter optimization system that can perform one or more black-box optimization techniques to iteratively suggest new sets of parameter values for evaluation. The iterative suggestion and evaluation process can serve to optimize or otherwise improve the overall performance of the system, as evaluated by an objective function that evaluates one or more metrics. The present disclosure also provides a novel black-box optimization technique known as “Gradientless Descent” that is more clever and faster than random search yet retains most of random search's favorable qualities.
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公开(公告)号：US20200167691A1
公开(公告)日：2020-05-28
申请号：US16615303
申请日：2017-06-02
Applicant: Google LLC
Inventor： Daniel Reuben Golovin , Benjamin Solnik , Subhodeep Moitra , David W. Sculley, II , Gregory Peter Kochanski
Abstract: A computer-implemented method can include receiving, by one or more computing devices, one or more prior evaluations of performance of a machine learning model, the one or more prior evaluations being respectively associated with one or more prior variants of the machine-learning model, the one or more prior variants of the machine-learning model each having been configured using a different set of adjustable parameter values. The method can include utilizing, by the one or more computing devices, an optimization algorithm to generate a suggested variant of the machine-learning model based at least in part on the one or more prior evaluations of performance and the associated set of adjustable parameter values, the suggested variant of the machine-learning model being defined by a suggested set of adjustable parameter values.
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公开(公告)号：US12032464B2
公开(公告)日：2024-07-09
申请号：US16495295
申请日：2017-06-02
Applicant: Google LLC
Inventor： Daniel Reuben Golovin , Benjamin Solnik , Subhodeep Moitra , David W. Sculley, II
CPC classification number: G06F11/3409 , G06F11/3006 , G06N5/01 , G06N7/01 , G06N20/00
Abstract: A computer-implemented method is provided for optimization of parameters of a system, product, or process. The method includes establishing an optimization procedure for a system, product, or process. The system, product, or process has an evaluable performance that is dependent on values of one or more adjustable parameters. The method includes receiving one or more prior evaluations of performance of the system, product, or process. The one or more prior evaluations are respectively associated with one or more prior variants of the system, product, or process. The one or more prior variants are each defined by a set of values for the one or more adjustable parameters. The method includes utilizing an optimization algorithm to generate a suggested variant based at least in part on the one or more prior evaluations of performance and the associated set of values.
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公开(公告)号：US12026612B2
公开(公告)日：2024-07-02
申请号：US16615303
申请日：2017-06-02
Applicant: Google LLC
Inventor： Daniel Reuben Golovin , Benjamin Solnik , Subhodeep Moitra , David W. Sculley, II , Gregory Peter Kochanski
Abstract: A computer-implemented method can include receiving, by one or more computing devices, one or more prior evaluations of performance of a machine learning model, the one or more prior evaluations being respectively associated with one or more prior variants of the machine-learning model, the one or more prior variants of the machine-learning model each having been configured using a different set of adjustable parameter values. The method can include utilizing, by the one or more computing devices, an optimization algorithm to generate a suggested variant of the machine-learning model based at least in part on the one or more prior evaluations of performance and the associated set of adjustable parameter values, the suggested variant of the machine-learning model being defined by a suggested set of adjustable parameter values.
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公开(公告)号：US20230342609A1
公开(公告)日：2023-10-26
申请号：US18347406
申请日：2023-07-05
Applicant: Google LLC
Inventor： Daniel Reuben Golovin , Benjamin Solnik , Subhodeep Moitra , David W. Sculley, II , Gregory Peter Kochanski
Abstract: The present disclosure provides computing systems and associated methods for optimizing one or more adjustable parameters (e.g. operating parameters) of a system. In particular, the present disclosure provides a parameter optimization system that can perform one or more black-box optimization techniques to iteratively suggest new sets of parameter values for evaluation. The iterative suggestion and evaluation process can serve to optimize or otherwise improve the overall performance of the system, as evaluated by an objective function that evaluates one or more metrics. The present disclosure also provides a novel black-box optimization technique known as “Gradientless Descent” that is more clever and faster than random search yet retains most of random search's favorable qualities.
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