The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Feb. 11, 2020

Filed:

Jul. 31, 2019
Applicant:

Sigopt, Inc., San Francisco, CA (US);

Inventors:

Bolong Cheng, San Francisco, CA (US);

Olivia Kim, San Francisco, CA (US);

Michael McCourt, San Francisco, CA (US);

Patrick Hayes, San Francisco, CA (US);

Scott Clark, San Francisco, CA (US);

Assignee:

SigOpt, Inc., San Francisco, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06N 99/00 (2019.01); G06N 7/00 (2006.01); G06K 9/00 (2006.01); G06F 3/048 (2013.01); H04L 12/24 (2006.01); G06N 20/00 (2019.01); G06F 17/10 (2006.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 17/10 (2013.01);
Abstract

Systems and methods for tuning hyperparameters of a model includes: receiving at a remote tuning service a multi-criteria tuning work request for tuning hyperparameters of the model of a subscriber, wherein the multi-criteria tuning work request includes: a first objective function of the model to be optimized by the remote tuning service; a second objective function to be optimized by the remote tuning service, the second objective function being distinct from the first objective function; computing a first conditionally constrained joint function for the model based on subjecting the first objective function to the second objective function; a second conditionally constrained joint function for the model based on subjecting the second objective function to the first objective function of the model; executing a tuning operation of the hyperparameters for the model; and identifying proposed hyperparameter values based on one or more hyperparameter-based points along a non-convex Pareto optimal curve.


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