图 书 馆
记忆有界稀疏线性回归的最小最大速率Minimax rates for memory-bounded sparse linear regression |
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| 课程网址: | http://videolectures.net/colt2015_steinhardt_linear_regression/ |
| 主讲教师: | Jacob Steinhardt |
| 开课单位: | 斯坦福大学 |
| 开课时间: | 2015-08-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We establish a minimax lower bound of on the number of samples needed to estimate the parameters in a given a memory bound of bits, where parameter error. When the covariance of the regressors is the identity matrix, we also provide an algorithm that uses samples to achieve error . Our lower bound also holds in the more general communication-bounded setting, where instead of a memory bound, at most bits of information are allowed to be (adaptively) communicated about each sample. |
| 关 键 词: | 内存边界; 估计维度; 内存限制 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-12-08：chenjy |
| 最后编审: | 2023-03-16：liyy |
| 阅读次数: | 20 |