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Algorithms for Robust and Memory-Efficient Learning.
Algorithms for Robust and Memory-Efficient Learning.
상세정보
- 자료유형
- 학위논문
- Control Number
- 0017163004
- International Standard Book Number
- 9798384448433
- Dewey Decimal Classification Number
- 004
- Main Entry-Personal Name
- Zhang, Fred.
- Publication, Distribution, etc. (Imprint
- [S.l.] : University of California, Berkeley., 2024
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- Physical Description
- 149 p.
- General Note
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- General Note
- Advisor: Nelson, Jelani.
- Dissertation Note
- Thesis (Ph.D.)--University of California, Berkeley, 2024.
- Summary, Etc.
- 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.
- Subject Added Entry-Topical Term
- Computer science.
- Index Term-Uncontrolled
- Algorithms
- Index Term-Uncontrolled
- Machine learning
- Index Term-Uncontrolled
- Robust estimation
- Index Term-Uncontrolled
- Memory-efficiency
- Added Entry-Corporate Name
- University of California, Berkeley Electrical Engineering & Computer Sciences
- Host Item Entry
- Dissertations Abstracts International. 86-03B.
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:655092
MARC
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aZhang, Fred.
■24510▼aAlgorithms for Robust and Memory-Efficient Learning.
■260 ▼a[S.l.]▼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.
