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Integrating Machine Learning and Optimization with Applications in Public Health and Sustainability.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Integrating Machine Learning and Optimization with Applications in Public Health and Sustainability./
作者:	Wang, Kai.
面頁冊數:	1 online resource (419 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-12, Section: A.
Contained By:	Dissertations Abstracts International84-12A.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30492027click for full text (PQDT)
ISBN:	9798379613426

Integrating Machine Learning and Optimization with Applications in Public Health and Sustainability.
Wang, Kai.

Integrating Machine Learning and Optimization with Applications in Public Health and Sustainability. - 1 online resource (419 pages)

Source: Dissertations Abstracts International, Volume: 84-12, Section: A.

Thesis (Ph.D.)--Harvard University, 2023.

Includes bibliographical references

The 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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379613426Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Decision-focused learningIndex Terms--Genre/Form:

542853
Electronic books.

Integrating Machine Learning and Optimization with Applications in Public Health and Sustainability.
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