arXiv:2607.07888v1 Announce Type: new 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
The increasing scale of data and the need for more efficient machine learning algorithms are driving research into distributed and optimized computational methods.
This development proposes a method to significantly reduce computational costs for large-scale data analysis, making advanced AI techniques more accessible and efficient for various applications.
Traditional approaches to OLS regression on large datasets can be made substantially more efficient and less resource-intensive through distributed sketching on partitioned data.
- · Big data analytics companies
- · Cloud computing providers
- · Researchers in distributed machine learning
- · Sectors with large datasets (e.g., finance, genomics)
- · Organizations using less efficient, centralized data processing methods
Reduced compute time and cost for large-scale linear regression tasks.
Enables new applications of OLS regression to previously intractable dataset sizes, potentially accelerating discoveries in data-heavy fields.
Could contribute to the broader trend of democratizing powerful analytical tools, allowing smaller entities to compete with larger ones on data-driven insights.
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Read at arXiv cs.LG