公开(公告)号：US20230325721A1

公开(公告)日：2023-10-12

申请号：US18320803

申请日：2023-05-19

Applicant: Intel Corporation

Inventor： Bolong Cheng ,  Olivia Kim ,  Michael McCourt ,  Patrick Hayes ,  Scott Clark

IPC: G06N20/00 ,  G06F17/10

CPC classification number: G06N20/00 ,  G06F17/10

Abstract: Systems and methods for tuning hyperparameters of a model includes: receiving a multi-criteria tuning work request for tuning hyperparameters of the model of the subscriber to the remote tuning service, 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 joint tuning func-tion based on a combination of the first objective function and the second objective function; executing a tuning opera-tion of the hyperparameters for the model based on a tuning of the joint function; and identifying one or more proposed hyperparameter values based on one or more hyperparam-eter-based points along a convex Pareto optimal curve.

公开(公告)号：US12033036B2

公开(公告)日：2024-07-09

申请号：US16943643

申请日：2020-07-30

Applicant: Intel Corporation

Inventor： Michael McCourt ,  Bolong Cheng ,  Taylor Jackie Spriggs ,  Halley Vance ,  Olivia Kim ,  Ben Hsu ,  Sarth Frey ,  Patrick Hayes ,  Scott Clark

IPC: G06N20/00 ,  G06F9/54 ,  G06F18/2115 ,  G06F18/214 ,  G06N20/20

CPC classification number: G06N20/00 ,  G06F9/541 ,  G06F18/2115 ,  G06F18/2148 ,  G06N20/20

Abstract: Systems and methods for tuning hyperparameters of a model include receiving a tuning request for tuning hyperparameters, the tuning request includes a first and a second objective function for the machine learning model. The first and second objective functions may output metric values that do not improve uniformly. Systems and methods additionally include defining a joint tuning function that is based on a combination of the first and second objective functions; executing a tuning operation; identifying a Pareto efficient frontier curve defined by a plurality of distinct hyperparameter values; applying metric thresholds to the Pareto efficient frontier curve; demarcating the Pareto efficient frontier curve into at least a first infeasible section and a second feasible section; searching the second feasible section of the Pareto efficient frontier curve for one or more proposed hyperparameter values; and identifying at least a first set of proposed hyperparameter values based on the search.

公开(公告)号：US11699098B2

公开(公告)日：2023-07-11

申请号：US16895099

申请日：2020-06-08

Applicant: Intel Corporation

Inventor： Bolong Cheng ,  Olivia Kim ,  Michael McCourt ,  Patrick Hayes ,  Scott Clark

IPC: G06N7/00 ,  G06N5/04 ,  G06N20/20 ,  G06N20/00 ,  G06F17/10

CPC classification number: G06N20/00 ,  G06F17/10

Abstract: Systems and methods for tuning hyperparameters of a model includes: receiving a multi-criteria tuning work request for tuning hyperparameters of the model of the subscriber to the remote tuning service, 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 joint tuning function based on a combination of the first objective function and the second objective function; executing a tuning operation of the hyperparameters for the model based on a tuning of the joint function; and identifying one or more proposed hyperparameter values based on one or more hyperparameter-based points along a convex Pareto optimal curve.