ROMO: Retrieval-enhanced Offline Model-based Optimization

doi:10.1145/3627676.3627685

	ROMO: Retrieval-enhanced Offline Model-based Optimization
	Chen, Mingcheng 1; Zhao, Haoran 1; Zhao, Yuxiang 2; Fan, Hulei 3; Gao, Hongqiao 3; Yu, Yong 1; Tian, Zheng4
	2023-11-30
会议录名称	ACM INTERNATIONAL CONFERENCE PROCEEDING SERIES
DOI	10.1145/3627676.3627685
摘要	Data-driven black-box model-based optimization (MBO) problems arise in a great number of practical application scenarios, where the goal is to find a design over the whole space maximizing a black-box target function based on a static offline dataset. In this work, we consider a more general but challenging MBO setting, named constrained MBO (CoMBO), where only part of the design space can be optimized while the rest is constrained by the environment. A new challenge arising from CoMBO is that most observed designs that satisfy the constraints are mediocre in evaluation. Therefore, we focus on optimizing these mediocre designs in the offline dataset while maintaining the given constraints rather than further boosting the best observed design in the traditional MBO setting. We propose retrieval-enhanced offline model-based optimization (ROMO), a new derivable forward approach that retrieves the offline dataset and aggregates relevant samples to provide a trusted prediction, and use it for gradient-based optimization. ROMO is simple to implement and outperforms state-of-The-Art approaches in the CoMBO setting. Empirically, we conduct experiments on a synthetic Hartmann (3D) function dataset, an industrial CIO dataset, and a suite of modified tasks in the Design-Bench benchmark. Results show that ROMO performs well in a wide range of constrained optimization tasks. © 2023 ACM.
关键词	Black box modelling Black-box optimization Data driven Model based optimization Off-line methods Offline Offline models Optimization problems Retrieval-enhanced ML Surrogate modeling
会议名称	5th International Conference on Distributed Artificial Intelligence, DAI 2023
会议地点	Singapore, Singapore
会议日期	November 30, 2023 - December 3, 2023
URL	查看原文
收录类别	EI
语种	英语
出版者	Association for Computing Machinery
EI入藏号	20240415423665
EI主题词	Constrained optimization
EI分类号	961 Systems Science
原始文献类型	Conference article (CA)
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/349524
专题	创意与艺术学院_PI研究组(P)_田政组
通讯作者	Tian, Zheng
作者单位	1.Shanghai Jiao Tong University, Shanghai, China 2.China Mobile Research Institute, Beijing, China 3.China Mobile (Zhejiang) Research and Innovation Institute, Hangzhou, China 4.ShanghaiTech University, Shanghai, China
通讯作者单位	上海科技大学
推荐引用方式 GB/T 7714	Chen, Mingcheng,Zhao, Haoran,Zhao, Yuxiang,et al. ROMO: Retrieval-enhanced Offline Model-based Optimization[C]:Association for Computing Machinery,2023.