Adaptive randomized pivoting and volume sampling
arXiv:2510.02513v2 Announce Type: replace Abstract: 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.
Statistics > Machine Learning
arXiv:2510.02513 (stat)
[Submitted on 2 Oct 2025 (v1), last revised 3 Apr 2026 (this version, v2)]
Title:Adaptive randomized pivoting and volume sampling
Authors:Ethan N. Epperly View a PDF of the paper titled Adaptive randomized pivoting and volume sampling, by Ethan N. Epperly View PDF
Abstract: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.
Comments: 14 pages, 2 figures
Subjects:
Machine Learning (stat.ML); Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Numerical Analysis (math.NA); Computation (stat.CO)
MSC classes: 65F55, 68W20
Cite as: arXiv:2510.02513 [stat.ML]
(or arXiv:2510.02513v2 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2510.02513
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arXiv-issued DOI via DataCite
Submission history
From: Ethan N. Epperly [view email] [v1] Thu, 2 Oct 2025 19:37:43 UTC (294 KB) [v2] Fri, 3 Apr 2026 16:47:38 UTC (299 KB)
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