On Robust Estimation in Causal Machine Learning
On Robust Estimation in Causal Machine Learning
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151014
- ISBN
- 9798381949766
- DDC
- 621.3
- 저자명
- Jiang, Jeffrey.
- 서명/저자
- On Robust Estimation in Causal Machine Learning
- 발행사항
- [Sl] : University of California, Los Angeles, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 228 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-09, Section: B.
- 주기사항
- Advisor: Pottie, Gregory.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2024.
- 초록/해제
- 요약This thesis presents three significant contributions to the field of machine learning, with a focus on Variational Autoencoders (VAEs), energy-based models, and education simulations. Firstly, we demonstrate the ability to impose substantial structure on the latent space of VAEs, enabling out-of-distribution data generation, structural hypothesis testing, and the production of augmentations in the latent space. These findings give us new ways to structure and interpret the latent space, creating robustness and explainability. Secondly, we identify a state-of-the-art defense technique using the unsupervised learning approach of energy-based models. This technique effectively defends against several poisoning techniques without requiring excessive additional training time or significantly reducing test accuracy. Lastly, we have developed a simulation for educational purposes that aims to model and comprehend the interactions between humans and machines. This simulation, built on causal information, provides insights into the design of practical educational experiments and highlights the challenges associated with implementing a dynamic Intelligent Tutoring System (ITS) in an educational context. Interestingly, our simulation reveals that heuristic methods continue to perform on par with deep learning techniques in the presence of unknown subpopulation distributions and hidden student states. This suggests that despite the rapid advancements in deep learning, heuristic methods retain their effectiveness in certain scenarios. These findings open new avenues for the application of machine learning techniques and provide a solid foundation for future research in these areas.
- 일반주제명
- Electrical engineering
- 일반주제명
- Computer engineering
- 키워드
- Causal reasoning
- 키워드
- Machine learning
- 키워드
- Simulation
- 기타저자
- University of California, Los Angeles Electrical and Computer Engineering 0333
- 기본자료저록
- Dissertations Abstracts International. 85-09B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798381949766
■035 ▼a(MiAaPQ)AAI30995648
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621.3
■1001 ▼aJiang, Jeffrey.
■24510▼aOn Robust Estimation in Causal Machine Learning
■260 ▼a[Sl]▼bUniversity of California, Los Angeles▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a228 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-09, Section: B.
■500 ▼aAdvisor: Pottie, Gregory.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2024.
■520 ▼aThis thesis presents three significant contributions to the field of machine learning, with a focus on Variational Autoencoders (VAEs), energy-based models, and education simulations. Firstly, we demonstrate the ability to impose substantial structure on the latent space of VAEs, enabling out-of-distribution data generation, structural hypothesis testing, and the production of augmentations in the latent space. These findings give us new ways to structure and interpret the latent space, creating robustness and explainability. Secondly, we identify a state-of-the-art defense technique using the unsupervised learning approach of energy-based models. This technique effectively defends against several poisoning techniques without requiring excessive additional training time or significantly reducing test accuracy. Lastly, we have developed a simulation for educational purposes that aims to model and comprehend the interactions between humans and machines. This simulation, built on causal information, provides insights into the design of practical educational experiments and highlights the challenges associated with implementing a dynamic Intelligent Tutoring System (ITS) in an educational context. Interestingly, our simulation reveals that heuristic methods continue to perform on par with deep learning techniques in the presence of unknown subpopulation distributions and hidden student states. This suggests that despite the rapid advancements in deep learning, heuristic methods retain their effectiveness in certain scenarios. These findings open new avenues for the application of machine learning techniques and provide a solid foundation for future research in these areas.
■590 ▼aSchool code: 0031.
■650 4▼aElectrical engineering
■650 4▼aComputer engineering
■653 ▼aCausal reasoning
■653 ▼aMachine learning
■653 ▼aRobust estimation
■653 ▼aSimulation
■653 ▼aUnsupervised learning
■690 ▼a0544
■690 ▼a0464
■690 ▼a0800
■71020▼aUniversity of California, Los Angeles▼bElectrical and Computer Engineering 0333.
■7730 ▼tDissertations Abstracts International▼g85-09B.
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160407▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
