Adaptive randomized pivoting (ARP) is a recently proposed and highly effective algorithm for column subset selection. This paper reinterprets the ARP algorithm by drawing connections to the volume sampling distribution and active learning algorithms for linear regression. As consequences, this paper presents new analysis for the ARP algorithm and faster implementations using rejection sampling.

翻译：自适应随机枢轴选取（ARP）是最近提出的一种用于列子集选择的高效算法。本文通过将ARP算法与体积采样分布以及线性回归的主动学习算法建立联系，对其进行了重新诠释。基于此，本文提出了对ARP算法的新分析，并利用拒绝采样实现了更快的执行方案。