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Scalable Gaussian Processes and Bayesian Optimization with Application to Hyperparameter Tuning
Scalable Gaussian Processes and Bayesian Optimization with Application to Hyperparameter Tuning
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211150940
- ISBN
- 9798382840833
- DDC
- 519
- 저자명
- Zhu, Xinran.
- 서명/저자
- Scalable Gaussian Processes and Bayesian Optimization with Application to Hyperparameter Tuning
- 발행사항
- [Sl] : Cornell University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 211 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Bindel, David.
- 학위논문주기
- Thesis (Ph.D.)--Cornell University, 2024.
- 초록/해제
- 요약This dissertation delves into the advanced realms of Gaussian Processes (GPs) and Bayesian Optimization (BO), presenting novel methodologies that enhance their performance and applicability. GPs, as a principled probabilistic approach, are powerful in modeling complex and noisy functions due to their non-parametric nature and capability for uncertainty quantification. However, exact GPs become intractable for large datasets since the computational cost scales cubically with the size of the dataset. In particular, this dissertation focuses on improving variational GPs, which is able to handle large-scale data by sparsifying the model via inducing points and approximating the posterior. Despite advances, variational GPs still may require many inducing points (and significant computational costs) to achieve good accuracy, a gap this dissertation aims to bridge.This dissertation also studies efficient computational methods for Bayesian transformed GPs (BTG), which is particularly useful when the Gaussian assumption is not satisfied and data is limited. Furthermore, the dissertation explores BO as a method for optimizing complex and expensive objective functions, with an emphasis on its application in hyperparameter tuning. By leveraging the probabilistic modeling strengths of GPs, BO can efficiently traverse the hyperparameter space, thus reducing the need for extensive model evaluations. Through the introduction of novel algorithms and methodologies, this research not only enhances the performance of BTG and variational GPs but also broadens the scope of BO in hyperparameter tuning.
- 일반주제명
- Applied mathematics
- 일반주제명
- Statistics
- 키워드
- Hyperparameters
- 키워드
- Parameter tuning
- 기타저자
- Cornell University Applied Mathematics
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798382840833
■035 ▼a(MiAaPQ)AAI30991702
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a519
■1001 ▼aZhu, Xinran.▼0(orcid)0000-0003-4988-0734
■24510▼aScalable Gaussian Processes and Bayesian Optimization with Application to Hyperparameter Tuning
■260 ▼a[Sl]▼bCornell University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a211 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Bindel, David.
■5021 ▼aThesis (Ph.D.)--Cornell University, 2024.
■520 ▼aThis dissertation delves into the advanced realms of Gaussian Processes (GPs) and Bayesian Optimization (BO), presenting novel methodologies that enhance their performance and applicability. GPs, as a principled probabilistic approach, are powerful in modeling complex and noisy functions due to their non-parametric nature and capability for uncertainty quantification. However, exact GPs become intractable for large datasets since the computational cost scales cubically with the size of the dataset. In particular, this dissertation focuses on improving variational GPs, which is able to handle large-scale data by sparsifying the model via inducing points and approximating the posterior. Despite advances, variational GPs still may require many inducing points (and significant computational costs) to achieve good accuracy, a gap this dissertation aims to bridge.This dissertation also studies efficient computational methods for Bayesian transformed GPs (BTG), which is particularly useful when the Gaussian assumption is not satisfied and data is limited. Furthermore, the dissertation explores BO as a method for optimizing complex and expensive objective functions, with an emphasis on its application in hyperparameter tuning. By leveraging the probabilistic modeling strengths of GPs, BO can efficiently traverse the hyperparameter space, thus reducing the need for extensive model evaluations. Through the introduction of novel algorithms and methodologies, this research not only enhances the performance of BTG and variational GPs but also broadens the scope of BO in hyperparameter tuning.
■590 ▼aSchool code: 0058.
■650 4▼aApplied mathematics
■650 4▼aStatistics
■653 ▼aGaussian processes
■653 ▼aBayesian optimization
■653 ▼aHyperparameters
■653 ▼aParameter tuning
■690 ▼a0364
■690 ▼a0463
■71020▼aCornell University▼bApplied Mathematics.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0058
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160236▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
