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Adaptive Bayesian Optimization for Organic Material Screening : 유기소재 스크리닝을 위한 적응적 베이지안 최적화
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 장병탁 | - |
dc.contributor.author | 윤상웅 | - |
dc.date.accessioned | 2017-07-19T08:39:50Z | - |
dc.date.available | 2017-07-19T08:39:50Z | - |
dc.date.issued | 2016-02 | - |
dc.identifier.other | 000000133465 | - |
dc.identifier.uri | https://hdl.handle.net/10371/131213 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 협동과정뇌과학전공, 2016. 2. 장병탁. | - |
dc.description.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. | - |
dc.description.tableofcontents | Chapter 1 Introduction 1
Chapter 2 Preliminaries 6 2.1 Bayesian Optimization 6 2.2 Multi-Armed Bandit 8 Chapter 3 Organic Material Screening: The Motivational Application 10 3.1 Beyond Structure-Property Relationship 10 3.2 Dataset: Electronic Properties of Organic Molecules 11 Chapter 4 Bayesian Optimization with Multiple Surrogate Functions 13 4.1 Problem Formulation 13 4.2 Baseline: Random Arm 14 4.3 Proposed Strategies 14 Chapter 5 Experiments 17 5.1 Benchmark Functions 18 5.2 Screening over Organic Molecules 18 Chapter 6 Discussion and Conclusion 23 6.1 Discussion 23 6.2 Conclusion 24 Bibliography 26 국문초록 30 | - |
dc.format | application/pdf | - |
dc.format.extent | 2624586 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | Bayesian Optimization | - |
dc.subject | Multi-Armed Bandit | - |
dc.subject | Chemoinformatics | - |
dc.subject.ddc | 611 | - |
dc.title | Adaptive Bayesian Optimization for Organic Material Screening | - |
dc.title.alternative | 유기소재 스크리닝을 위한 적응적 베이지안 최적화 | - |
dc.type | Thesis | - |
dc.contributor.AlternativeAuthor | Sangwoong Yoon | - |
dc.description.degree | Master | - |
dc.citation.pages | 30 | - |
dc.contributor.affiliation | 자연과학대학 협동과정뇌과학전공 | - |
dc.date.awarded | 2016-02 | - |
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