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Theoretical Foundations of Trustworthy Machine Learning- [electronic resource]
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Theoretical Foundations of Trustworthy Machine Learning- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101518
- ISBN
- 9798380417419
- DDC
- 621.3
- 서명/저자
- Theoretical Foundations of Trustworthy Machine Learning - [electronic resource]
- 발행사항
- [S.l.]: : University of California, San Diego., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(270 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
- 주기사항
- Advisor: Chaudhuri, Kamalika.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Diego, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Machine learning models have become a ubiquitous part of society, and it has consequently become of paramount importance to understand how to design safe and reliable models. This dissertation attempts to take steps towards this direction by consider two specific problems in reliable machine learning: adversarial examples, which are small test-time perturbations to the input designed to cause misclassification, and data-copying, which occurs when a generative model simply memorizes its training data (giving poor generalization and dangerous security risks).
- 일반주제명
- Computer engineering.
- 일반주제명
- Computer science.
- 키워드
- Machine learning
- 키워드
- Data-copying
- 키워드
- Training data
- 키워드
- Security risks
- 기타저자
- University of California, San Diego Computer Science and Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-04B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214101518
■006m o d
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■020 ▼a9798380417419
■035 ▼a(MiAaPQ)AAI30569674
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621.3
■1001 ▼aBhattacharjee, Robi.
■24510▼aTheoretical Foundations of Trustworthy Machine Learning▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of California, San Diego. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(270 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-04, Section: B.
■500 ▼aAdvisor: Chaudhuri, Kamalika.
■5021 ▼aThesis (Ph.D.)--University of California, San Diego, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aMachine learning models have become a ubiquitous part of society, and it has consequently become of paramount importance to understand how to design safe and reliable models. This dissertation attempts to take steps towards this direction by consider two specific problems in reliable machine learning: adversarial examples, which are small test-time perturbations to the input designed to cause misclassification, and data-copying, which occurs when a generative model simply memorizes its training data (giving poor generalization and dangerous security risks).
■590 ▼aSchool code: 0033.
■650 4▼aComputer engineering.
■650 4▼aComputer science.
■653 ▼aMachine learning
■653 ▼aReliable machine learning
■653 ▼aMisclassification
■653 ▼aData-copying
■653 ▼aTraining data
■653 ▼aSecurity risks
■690 ▼a0800
■690 ▼a0984
■690 ▼a0464
■71020▼aUniversity of California, San Diego▼bComputer Science and Engineering.
■7730 ▼tDissertations Abstracts International▼g85-04B.
■773 ▼tDissertation Abstract International
■790 ▼a0033
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16934016▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
