Distributed Sketching on Data Partitions for OLS Regression
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Computer Science > Machine Learning
Title:Distributed Sketching on Data Partitions for OLS Regression
Abstract:This paper studies distributed sketching for ordinary least squares (OLS) regression, an approach that distributes small sketches of a large data set over multiple machines to separately construct OLS estimators and average them. Unlike prior studies that consider sketching on the whole data set, we consider sketching on partitioned subsets to further reduce computational cost. Under the fixed design setting, we characterize the exact excess loss of the averaged OLS estimator. Results show that this loss is comparable to the established loss for sketching on the whole data set when the divergence among subset covariances is small.
| Comments: | This work has been accepted at Statistics&Probability Letters, 2026 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.07888 [cs.LG] |
| (or arXiv:2607.07888v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.07888
arXiv-issued DOI via DataCite (pending registration)
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