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Towards Trustworthy Machine Learning: An Integer Programming Approach
Towards Trustworthy Machine Learning: An Integer Programming Approach
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151316
- ISBN
- 9798382841786
- DDC
- 621.3
- 서명/저자
- Towards Trustworthy Machine Learning: An Integer Programming Approach
- 발행사항
- [Sl] : Cornell University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 288 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Gunluk, Oktay.
- 학위논문주기
- Thesis (Ph.D.)--Cornell University, 2024.
- 초록/해제
- 요약Despite the proliferation of machine learning (ML) in a multitude of applications, current black-box models, such as deep learning, remain hard to understand, critique, and judge by decision makers. This in turn limits their adoption in high-stakes environments (e.g., credit lending, college admissions, medicine) where ML is often used as a tool to support a human decision maker. Moreover in applications where decisions have a significant societal impact, practitioners need ML models that can guarantee that their output is fair to sensitive demographic groups, a challenging constraint to integrate into existing algorithms. Integer Programming (IP) is a natural tool for these problems as many simple interpretable ML models can be represented by low-complexity discrete objects, and it allows for the flexible incorporation of domain-specific constraints such as fairness criteria. However, despite its success in numerous industrial applications, such as scheduling and logistics, exact IP methods are considered to be too computationally demanding to be used in many ML applications and are eschewed for fast heuristics. This thesis endeavors to bridge this gap between exact optimization and fast heuristics by leveraging large-scale integer programming techniques to build scalable algorithms that can outperform existing ML heuristics in a fraction of the time of exact IP-based methods. In particular, this thesis develops novel formulations for ML problems built upon strong combinatorial structure that can flexibly incorporate domain-specific constraints such as fairness. Underpinning these formulations are large-scale optimization procedures that are informed by exact optimization methods but leverage heuristics tailored for ML settings that allow them to scale to large data sets. Finally, towards democratizing these IP-based machine learning tools, this thesis explores how to leverage Large Language Models to enable non-expert users to interact and customize mathematical optimization models.
- 일반주제명
- Computer engineering
- 키워드
- Clustering
- 키워드
- Fairness
- 키워드
- Machine learning
- 기타저자
- Cornell University Operations Research and Information Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■1001 ▼aLawless, Connor Aram.▼0(orcid)0000-0002-2112-2213
■24510▼aTowards Trustworthy Machine Learning: An Integer Programming Approach
■260 ▼a[Sl]▼bCornell University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a288 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Gunluk, Oktay.
■5021 ▼aThesis (Ph.D.)--Cornell University, 2024.
■520 ▼aDespite the proliferation of machine learning (ML) in a multitude of applications, current black-box models, such as deep learning, remain hard to understand, critique, and judge by decision makers. This in turn limits their adoption in high-stakes environments (e.g., credit lending, college admissions, medicine) where ML is often used as a tool to support a human decision maker. Moreover in applications where decisions have a significant societal impact, practitioners need ML models that can guarantee that their output is fair to sensitive demographic groups, a challenging constraint to integrate into existing algorithms. Integer Programming (IP) is a natural tool for these problems as many simple interpretable ML models can be represented by low-complexity discrete objects, and it allows for the flexible incorporation of domain-specific constraints such as fairness criteria. However, despite its success in numerous industrial applications, such as scheduling and logistics, exact IP methods are considered to be too computationally demanding to be used in many ML applications and are eschewed for fast heuristics. This thesis endeavors to bridge this gap between exact optimization and fast heuristics by leveraging large-scale integer programming techniques to build scalable algorithms that can outperform existing ML heuristics in a fraction of the time of exact IP-based methods. In particular, this thesis develops novel formulations for ML problems built upon strong combinatorial structure that can flexibly incorporate domain-specific constraints such as fairness. Underpinning these formulations are large-scale optimization procedures that are informed by exact optimization methods but leverage heuristics tailored for ML settings that allow them to scale to large data sets. Finally, towards democratizing these IP-based machine learning tools, this thesis explores how to leverage Large Language Models to enable non-expert users to interact and customize mathematical optimization models.
■590 ▼aSchool code: 0058.
■650 4▼aComputer engineering
■653 ▼aClustering
■653 ▼aFairness
■653 ▼aInteger Programming
■653 ▼aMachine learning
■690 ▼a0796
■690 ▼a0464
■690 ▼a0800
■71020▼aCornell University▼bOperations Research and Information Engineering.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0058
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161146▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
