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Principled Exploration in Sequential Decision-Making
Principled Exploration in Sequential Decision-Making
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105655
- ISBN
- 9798265453266
- DDC
- 004
- 저자명
- Ban, Yikun.
- 서명/저자
- Principled Exploration in Sequential Decision-Making
- 발행사항
- [Sl] : University of Illinois at Urbana-Champaign, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 162 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-06, Section: A.
- 주기사항
- Advisor: He, Jingrui.
- 학위논문주기
- Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2024.
- 초록/해제
- 요약Interactive Machine Learning (IML) possesses the unique capability to harness feedback from interactions, making it indispensable in a wide array of real-world applications. However, a significant challenge, known as the "exploitation and exploration dilemma," prominently arises within the domain of IML. In this context, learners must not only exploit current information but also explore to uncover potential knowledge for long-term gains. Despite decades of research yielding a rich landscape of algorithms, frameworks, and theories for effectively utilizing collected data to train machine learning models, a fundamental question has remained largely unaddressed: How can IML models systematically make principled exploration for long-term benefits, alongside the full exploitation of current data? This thesis will motivate the exploration of IML by human principles in sequential decision-making, and then present our research efforts in developing principled exploration strategies, including adaptive exploration, collaborative exploration, and customized exploration, and the future directions in trustworthy exploration. The content of this thesis will cover the fundamental algorithms and theories in exploration and show how the exploration strategies impact other machine learning problems and real-world applications.
- 일반주제명
- Computer science
- 일반주제명
- Computer engineering
- 일반주제명
- Information science
- 키워드
- Neural networks
- 기타저자
- University of Illinois at Urbana-Champaign Computer Science
- 기본자료저록
- Dissertations Abstracts International. 87-06A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798265453266
■035 ▼a(MiAaPQ)AAI32409661
■035 ▼a(MiAaPQ)124489
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aBan, Yikun.
■24510▼aPrincipled Exploration in Sequential Decision-Making
■260 ▼a[Sl]▼bUniversity of Illinois at Urbana-Champaign▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a162 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-06, Section: A.
■500 ▼aAdvisor: He, Jingrui.
■5021 ▼aThesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2024.
■520 ▼aInteractive Machine Learning (IML) possesses the unique capability to harness feedback from interactions, making it indispensable in a wide array of real-world applications. However, a significant challenge, known as the "exploitation and exploration dilemma," prominently arises within the domain of IML. In this context, learners must not only exploit current information but also explore to uncover potential knowledge for long-term gains. Despite decades of research yielding a rich landscape of algorithms, frameworks, and theories for effectively utilizing collected data to train machine learning models, a fundamental question has remained largely unaddressed: How can IML models systematically make principled exploration for long-term benefits, alongside the full exploitation of current data? This thesis will motivate the exploration of IML by human principles in sequential decision-making, and then present our research efforts in developing principled exploration strategies, including adaptive exploration, collaborative exploration, and customized exploration, and the future directions in trustworthy exploration. The content of this thesis will cover the fundamental algorithms and theories in exploration and show how the exploration strategies impact other machine learning problems and real-world applications.
■590 ▼aSchool code: 0090.
■650 4▼aComputer science
■650 4▼aComputer engineering
■650 4▼aInformation science
■653 ▼aMulti-armed bandits
■653 ▼aContextual bandits
■653 ▼aNeural networks
■653 ▼aInteractive Machine Learning
■690 ▼a0984
■690 ▼a0464
■690 ▼a0723
■71020▼aUniversity of Illinois at Urbana-Champaign▼bComputer Science.
■7730 ▼tDissertations Abstracts International▼g87-06A.
■790 ▼a0090
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17361032▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
