DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 김종환 | - |
dc.contributor.author | Han, Guk | - |
dc.contributor.author | 한국 | - |
dc.date.accessioned | 2024-07-30T19:30:13Z | - |
dc.date.available | 2024-07-30T19:30:13Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1052061&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/321237 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 전기및전자공학부, 2023.2,[iv, 56 p. :] | - |
dc.description.abstract | The industrial field faces the problem of process optimization by finding the factors affecting the yield of the process and controlling them appropriately. However, due to limited resources such as time and money, optimization is performed using a low evaluation budget. In addition, for process stability, the lower limit of the yield is set so that the yield must be maintained above this limit during optimization. Bayesian Optimization (BO) can be an effective solution in acquiring optimal samples that satisfy a safety constraint given a low evaluation budget. However, many existing BO algorithms have some limitations such as significant performance degradation due to model misspecification, and high computational load. Thus, we propose a practical safe BO algorithm, A-SafeBO, that effectively reduces performance degradation due to model misspecification using only a limited evaluation budget. Additionally, our algorithm performs computations for a large number of observations and high-dimensional input spaces by using Ensemble Gaussian Processes and Safe Particle Swarm Optimization. Here, we also propose a new acquisition function that leads to a wider exploration even under the constraint of safety. This will help deviate from the local optimum and achieve a better recommendation. Our algorithm empirically guarantees convergence and performance through evaluations on several synthetic benchmarks and a real-world optimization problem. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 베이지안 최적화▼a가우시안 프로세스▼a안전 탐사▼a초매개변수 조정▼a산업 응용▼a낮은 평가 예산 | - |
dc.subject | Bayesian optimization▼aGaussian process▼aSafe exploration▼aHyperparameter tuning▼aIndustrial application▼aLow evaluation budget | - |
dc.title | Adaptive bayesian optimization for fast and extensive search under safety constraints | - |
dc.title.alternative | 안전 제약 조건에서 빠르고 광범위한 탐색을 위한 적응형 베이지안 최적화 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | Jonghwan Kim | - |
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