SHERP

Adaptive Bayesian Optimization for Organic Material Screening
유기소재 스크리닝을 위한 적응적 베이지안 최적화

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Authors
윤상웅
Advisor
장병탁
Major
자연과학대학 협동과정뇌과학전공
Issue Date
2016
Publisher
서울대학교 대학원
Keywords
Bayesian OptimizationMulti-Armed BanditChemoinformatics
Description
학위논문 (석사)-- 서울대학교 대학원 : 협동과정뇌과학전공, 2016. 2. 장병탁.
Abstract
Bayesian optimization (BO) is an efficient black-box optimization method which utilizes the power of statistical models built upon previously searched points. The efficacy of BO largely depends on the choice of the statistical model, but it is usually difficult to determine beforehand which model would yield the best optimization performance for a given task. This thesis investigates a modified problem setting for BO where multiple candidate surrogate functions are available, and experiments two novel strategies based on multi-armed bandit algorithms. The proposed strategies attempt to discriminate among the candidate models, and therefore referred as adaptive BO's. The strategies are tested on optimization test bed functions, and the chemical screening scheduling problem where the issue of selecting a surrogate function to use become particularly salient. Surprisingly, it is discovered that the baseline strategy which blends multiple candidate functions uniform-randomly performs non-trivial performance. The results presented in the thesis shows that the relaxation of the number of surrogate functions in BO yields interesting dynamics.
Language
English
URI
http://hdl.handle.net/10371/131213
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College of Natural Sciences (자연과학대학)Program in Brain Science (협동과정-뇌과학전공)Theses (Master's Degree_협동과정-뇌과학전공)
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