专利检索 ap:("President AND Fellows of Harvard College" OR "The Governing Council of the University of Toronto" OR "SOCPRA Sciences et Génie s.e.c.") AND inv:"SNOEK, Roland, Jasper" 第 1 页

1.

发明公开
SYSTEMS AND METHODS FOR PERFORMING BAYESIAN OPTIMIZATION 审中-公开
标题翻译：系统和用于进行贝叶斯优化方法

公开(公告)号：EP3000053A2

公开(公告)日：2016-03-30

申请号：EP14804134.6

申请日：2014-05-30

申请人： President and Fellows of Harvard College , The Governing Council of the University of Toronto , SOCPRA Sciences et Génie s.e.c.

发明人： ADAMS, Ryan, P. , SNOEK, Roland, Jasper , LAROCHELLE, Hugo , SWERSKY, Kevin , ZEMEL, Richard

IPC分类号： G06F15/18

CPC分类号： G06N5/048 , G06F17/11 , G06N7/005 , G06N99/005

摘要： Techniques for use in connection with performing optimization using a plurality of objective functions associated with a respective plurality of tasks. The techniques include using at least one computer hardware processor to perform: identifying, based at least in part on a joint probabilistic model of the plurality of objective functions, a first point at which to evaluate an objective function in the plurality of objective functions; selecting, based at least in part on the joint probabilistic model, a first objective function in the plurality of objective functions to evaluate at the identified first point; evaluating the first objective function at the identified first point; and updating the joint probabilistic model based on results of the evaluation to obtain an updated joint probabilistic model.

2.

发明公开
SYSTEMS AND METHODS FOR PERFORMING BAYESIAN OPTIMIZATION 审中-公开

公开(公告)号：EP4047530A1

公开(公告)日：2022-08-24

申请号：EP22166308.1

申请日：2014-05-30

申请人： President and Fellows of Harvard College , The Governing Council of the University of Toronto , SOCPRA Sciences et Génie s.e.c.

发明人： ADAMS, Ryan, P. , SNOEK, Roland, Jasper , LAROCHELLE, Hugo , SWERSKY, Kevin , ZEMEL, Richard

IPC分类号： G06N7/00 , G06N20/10

摘要： Techniques for use in connection with performing optimization using a plurality of objective functions associated with a respective plurality of tasks. The techniques include using at least one computer hardware processor to perform: identifying, based at least in part on a joint probabilistic model of the plurality of objective functions, a first point at which to evaluate an objective function in the plurality of objective functions; selecting, based at least in part on the joint probabilistic model, a first objective function in the plurality of objective functions to evaluate at the identified first point; evaluating the first objective function at the identified first point; and updating the joint probabilistic model based on results of the evaluation to obtain an updated joint probabilistic model.