Adaptive randomized pivoting and volume sampling
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910100710490112 |
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| author | Epperly, Ethan N. |
| author_facet | Epperly, Ethan N. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02513 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive randomized pivoting and volume sampling Epperly, Ethan N. Machine Learning Data Structures and Algorithms Numerical Analysis Computation 65F55, 68W20 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. |
| title | Adaptive randomized pivoting and volume sampling |
| topic | Machine Learning Data Structures and Algorithms Numerical Analysis Computation 65F55, 68W20 |
| url | https://arxiv.org/abs/2510.02513 |