图 书 馆
基于凸松弛的计算与统计推算Computational and Statistical Tradeoffs via Convex Relaxation |
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| 课程网址: | http://videolectures.net/nipsworkshops2012_chandrasekaran_convex_... |
| 主讲教师: | Venkat Chandrasekaran |
| 开课单位: | 加州理工学院 |
| 开课时间: | 2013-01-16 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In modern data analysis, one is frequently faced with statistical inference problems involving massive datasets. Processing such large datasets is usually viewed as a substantial computational challenge. However, if data are a statistician's main resource then access to more data should be viewed as an asset rather than as a burden. We describe a computational framework based on convex relaxation to reduce the computational complexity of an inference procedure when one has access to increasingly larger datasets. Convex relaxation techniques have been widely used in theoretical computer science as they give tractable approximation algorithms to many computationally intractable tasks. We demonstrate the efficacy of this methodology in statistical estimation in providing concrete time-data tradeoffs in a class of denoising problems. Thus, convex relaxation offers a principled approach to exploit the statistical gains from larger datasets to reduce the runtime of inference algorithms. (Joint work with Michael Jordan) |
| 关 键 词: | 数据集; 统计推断; 凸松弛技术 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-08：lxf |
| 阅读次数: | 66 |