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Distributionally Robust Learning Under the Wasserstein Metric
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Distributionally Robust Learning Under the Wasserstein Metric. |
| 개인저자 | Chen, Ruidi. |
| 단체저자명 | Boston University. Systems Engineering ENG. |
| 발행사항 | [S.l.]: Boston University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 206 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-06B. Dissertation Abstract International |
| ISBN | 9781687992345 |
| 학위논문주기 | Thesis (Ph.D.)--Boston University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-06, Section: B.
Advisor: Paschalidis, Ioannis Ch. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | This dissertation develops a comprehensive statistical learning framework that is robust to (distributional) perturbations in the data using Distributionally Robust Optimization (DRO) under the Wasserstein metric. The learning problems that are studied include: (i) Distributionally Robust Linear Regression (DRLR), which estimates a robustified linear regression plane by minimizing the worst-case expected absolute loss over a probabilistic ambiguity set characterized by the Wasserstein metric |
| 일반주제명 | Statistics. Artificial intelligence. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
