图 书 馆
记忆有界稀疏线性回归的极大极小率Minimax rates for memory-bounded sparse linear regression |
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| 课程网址: | https://videolectures.net/videos/colt2015_steinhardt_linear_regre... |
| 主讲教师: | Jacob Steinhardt |
| 开课单位: | 信息不详。欢迎您在右侧留言补充。 |
| 开课时间: | 2025-02-04 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We establish a minimax lower bound of $\Omega\p{\frac{kd}{b\epsilon}}$ on the number of samples needed to estimate the parameters in a $k$-sparse linear regression of dimension $d$ given a memory bound of $b$ bits, where $\epsilon$ is the $L_2$ parameter error. When the covariance of the regressors is the identity matrix, we also provide an algorithm that uses $\tilde{O}(b+k)$ bits and requires $\tilde{O}(\frac{kd}{b\epsilon^2})$ samples to achieve error $\epsilon$. Our lower bound also holds in the more general communication-bounded setting, where instead of a memory bound, at most $b$ bits of information are allowed to be (adaptively) communicated about each sample |
| 关 键 词: | 极小极大下; 稀疏线性回; 协方差 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2025-03-28：zsp |
| 最后编审: | 2025-03-28：zsp |
| 阅读次数: | 5 |