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Algorithms for Robust and Memory-Efficient Learning
Algorithms for Robust and Memory-Efficient Learning
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152122
- ISBN
- 9798384448433
- DDC
- 004
- 저자명
- Zhang, Fred.
- 서명/저자
- Algorithms for Robust and Memory-Efficient Learning
- 발행사항
- [Sl] : University of California, Berkeley, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 149 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- 주기사항
- Advisor: Nelson, Jelani.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2024.
- 초록/해제
- 요약Modern machine learning (ML) processes massive data. The thesis tackles two algorithmic challenges arising from large-scale ML-robustness to noisy training data and memory-efficiency of the learning algorithms. Motivated by the first, I propose (i) the fastest algorithm for learning the mean of high-dimensional heavy-tailed distribution, (ii) a unified analysis framework for robust estimation, and (iii) efficient and robust algorithm for privately estimating high dimensional Gaussian. For memory-efficiency, I give the first sub-linear space algorithm for online prediction, the most classic problem in sequential learning.
- 일반주제명
- Computer science
- 키워드
- Algorithms
- 키워드
- Machine learning
- 기타저자
- University of California, Berkeley Electrical Engineering & Computer Sciences
- 기본자료저록
- Dissertations Abstracts International. 86-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798384448433
■035 ▼a(MiAaPQ)AAI31481886
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aZhang, Fred.
■24510▼aAlgorithms for Robust and Memory-Efficient Learning
■260 ▼a[Sl]▼bUniversity of California, Berkeley▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a149 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: B.
■500 ▼aAdvisor: Nelson, Jelani.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2024.
■520 ▼aModern machine learning (ML) processes massive data. The thesis tackles two algorithmic challenges arising from large-scale ML-robustness to noisy training data and memory-efficiency of the learning algorithms. Motivated by the first, I propose (i) the fastest algorithm for learning the mean of high-dimensional heavy-tailed distribution, (ii) a unified analysis framework for robust estimation, and (iii) efficient and robust algorithm for privately estimating high dimensional Gaussian. For memory-efficiency, I give the first sub-linear space algorithm for online prediction, the most classic problem in sequential learning.
■590 ▼aSchool code: 0028.
■650 4▼aComputer science
■653 ▼aAlgorithms
■653 ▼aMachine learning
■653 ▼aRobust estimation
■653 ▼aMemory-efficiency
■690 ▼a0984
■690 ▼a0796
■690 ▼a0800
■71020▼aUniversity of California, Berkeley▼bElectrical Engineering & Computer Sciences.
■7730 ▼tDissertations Abstracts International▼g86-03B.
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163004▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
