| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000437700 |
| 005 | | 20200303153659 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781687992345 |
| 035 | |
▼a (MiAaPQ)AAI27539670 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 001 |
| 100 | 1 |
▼a Chen, Ruidi. |
| 245 | 10 |
▼a Distributionally Robust Learning Under the Wasserstein Metric. |
| 260 | |
▼a [S.l.]:
▼b Boston University.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 206 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-06, Section: B. |
| 500 | |
▼a Advisor: Paschalidis, Ioannis Ch. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Boston University, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0017. |
| 650 | 4 |
▼a Statistics. |
| 650 | 4 |
▼a Artificial intelligence. |
| 690 | |
▼a 0463 |
| 690 | |
▼a 0800 |
| 710 | 20 |
▼a Boston University.
▼b Systems Engineering ENG. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-06B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0017 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15494374
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |