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Modern Optimization for Statistics and Learning
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Modern Optimization for Statistics and Learning. |
| 개인저자 | Eisenach, Carson Mark. |
| 단체저자명 | Princeton University. Operations Research and Financial Engineering. |
| 발행사항 | [S.l.]: Princeton University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 215 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-02B. Dissertation Abstract International |
| ISBN | 9781085628105 |
| 학위논문주기 | Thesis (Ph.D.)--Princeton University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-02, Section: B.
Advisor: Liu, Han. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Traditional problems in statistics and machine learning are relatively well understood - they often feature low dimensionality, convex loss functions, and independent, identically distributed data. By contrast, many modern learning problems feature high dimensional data, non-convex learning objectives, and data distributions that change during the learning process. Whether the problem of interest is labeled as statistics, machine learning, statistical learning, or reinforcement learning, methods for solving it can be viewed as the stochastic optimization of some objective function.Accordingly, we address the aforementioned challenges via the lens of statistical optimization - a statistical approach for understanding and solving stochastic optimization. In particular, we focus on deriving new methodology with computational and statistical guarantees for two classes of problems: recovering and performing inference on latent patterns in high-dimensional graphical models, and continuous control over bounded action spaces.In the first part of this dissertation, we consider a class of cluster-based graphical models. We introduce a novel algorithm for variable clustering named FORCE, based on solving a convex relaxation of the K-means criterion, as well as post-dimension reduction inferential procedures. In the second part, we consider the reinforcement learning (RL) setting, where an agent seeks to learn a decision-making policy based on feedback from its environment. We derive a novel class of variance-reduced estimators called Marginal Policy Gradients, and demonstrate both their improved statistical properties and their application to several control tasks. |
| 일반주제명 | Statistics. Computer science. Artificial intelligence. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
