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Integrating Machine Learning and Optimization with Applications in Public Health and Sustainability- [electronic resource]
Integrating Machine Learning and Optimization with Applications in Public Health and Sustainability- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214100451
- ISBN
- 9798379613426
- DDC
- 004
- 저자명
- Wang, Kai.
- 서명/저자
- Integrating Machine Learning and Optimization with Applications in Public Health and Sustainability - [electronic resource]
- 발행사항
- [S.l.]: : Harvard University., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(419 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 84-12, Section: A.
- 주기사항
- Advisor: Tambe, Milind.
- 학위논문주기
- Thesis (Ph.D.)--Harvard University, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약The field of artificial intelligence (AI) has garnered increasing attention in the realms of public health and conservation due to its potential to characterize complex dynamics and facilitate difficult decision-making. My research focuses on developing AI solutions, utilizing machine learning and optimization techniques, to provide actionable decisions for deployment and create positive social impact. This endeavor necessitates the integration of new algorithmic and learning paradigms, combining machine learning techniques to extract knowledge from data and optimization techniques to leverage domain knowledge and scale up to larger problem sizes. In this thesis, I present methodological and theoretical contributions in the integration of optimization into machine learning problems, including supervised learning, online learning, and multi-agent systems, with the aim of improving learning performance and scalability by harnessing the knowledge encoded in optimization tasks. Notably, this thesis introduces the first decision-focused learning to integrate sequential problems into the learning pipeline to provide feedback from decision-making processes and significantly reduce computation costs, thus enabling applications in large-scale public health problems. The proposed algorithm has been successfully applied in a field study and deployment in a maternal and child health program, marking the first successful implementation of decision-focused learning in the real world. Currently, the proposed algorithm is used by over 100,000 beneficiaries in India to enhance engagement with health information and translate algorithmic contributions into tangible social impact.
- 일반주제명
- Computer science.
- 일반주제명
- Public health.
- 일반주제명
- Sustainability.
- 키워드
- Machine learning
- 키워드
- Online learning
- 키워드
- Optimization
- 키워드
- Social impact
- 기타저자
- Harvard University Engineering and Applied Sciences - Computer Science
- 기본자료저록
- Dissertations Abstracts International. 84-12A.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798379613426
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■1001 ▼aWang, Kai.▼0(orcid)0000-0002-2446-987X
■24510▼aIntegrating Machine Learning and Optimization with Applications in Public Health and Sustainability▼h[electronic resource]
■260 ▼a[S.l.]:▼bHarvard University. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(419 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 84-12, Section: A.
■500 ▼aAdvisor: Tambe, Milind.
■5021 ▼aThesis (Ph.D.)--Harvard University, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aThe field of artificial intelligence (AI) has garnered increasing attention in the realms of public health and conservation due to its potential to characterize complex dynamics and facilitate difficult decision-making. My research focuses on developing AI solutions, utilizing machine learning and optimization techniques, to provide actionable decisions for deployment and create positive social impact. This endeavor necessitates the integration of new algorithmic and learning paradigms, combining machine learning techniques to extract knowledge from data and optimization techniques to leverage domain knowledge and scale up to larger problem sizes. In this thesis, I present methodological and theoretical contributions in the integration of optimization into machine learning problems, including supervised learning, online learning, and multi-agent systems, with the aim of improving learning performance and scalability by harnessing the knowledge encoded in optimization tasks. Notably, this thesis introduces the first decision-focused learning to integrate sequential problems into the learning pipeline to provide feedback from decision-making processes and significantly reduce computation costs, thus enabling applications in large-scale public health problems. The proposed algorithm has been successfully applied in a field study and deployment in a maternal and child health program, marking the first successful implementation of decision-focused learning in the real world. Currently, the proposed algorithm is used by over 100,000 beneficiaries in India to enhance engagement with health information and translate algorithmic contributions into tangible social impact.
■590 ▼aSchool code: 0084.
■650 4▼aComputer science.
■650 4▼aPublic health.
■650 4▼aSustainability.
■653 ▼aDecision-focused learning
■653 ▼aMachine learning
■653 ▼aOnline learning
■653 ▼aOptimization
■653 ▼aSocial impact
■690 ▼a0984
■690 ▼a0800
■690 ▼a0640
■690 ▼a0573
■71020▼aHarvard University▼bEngineering and Applied Sciences - Computer Science.
■7730 ▼tDissertations Abstracts International▼g84-12A.
■773 ▼tDissertation Abstract International
■790 ▼a0084
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16932382▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
