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Statistical Inference and the Sum of Squares Method
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Statistical Inference and the Sum of Squares Method. |
| 개인저자 | Hopkins, Samuel. |
| 단체저자명 | Cornell University. Computer Science. |
| 발행사항 | [S.l.]: Cornell University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 430 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-01B(E). Dissertation Abstract International |
| ISBN | 9780438345003 |
| 학위논문주기 | Thesis (Ph.D.)--Cornell University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: David Steurer. |
| 요약 | Statistical inference on high-dimensional and noisy data is a central concern of modern computer science. Often, the main challenges are inherently computational: the problems are well understood from a purely statistical perspective, but key st |
| 요약 | We develop a unified approach to algorithm design for statistical inference based on the Sum of Squares method, a powerful tool for convex programming with low-degree polynomials, which generalizes linear programming and spectral algorithms. We |
| 요약 | We also prove computational lower bounds for some statistical problems, including the long-studied planted clique problem. Our lower bounds provide new strong evidence for the existence of information-computation gaps -- that is, statistical pro |
| 요약 | We show that polynomial-size semidefinite programs from the Sum of Squares hierarchy cannot refute the existence of cliques of size much less than the square root of n in n-node random graphs. Additionally, we prove a lower bound for sparse prin |
| 요약 | Our approach to algorithms and lower bounds suggests a new method to chart the edge of algorithmic tractability for statistical inference. We propose a classification of Bayesian inference problems according to solvability by algorithms which co |
| 일반주제명 | Computer science. Mathematics. Statistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
