Sequential Least-Squares Estimators with Fast Randomized Sketching for Linear Statistical Models
arXiv:2509.06856v2 Announce Type: replace-cross Abstract: We propose a novel randomized framework for the estimation problem of large-scale linear statistical models, namely Sequential Least-Squares Estimators with Fast Randomized Sketching (SLSE-FRS), which integrates Sketch-and-Solve and Iterative-Sketching methods for the first time. By iteratively constructing and solving sketched least-squares (LS) subproblems with increasing sketch sizes to achieve better precisions, SLSE-FRS gradually refines the estimators of the true parameter vector, ultimately producing high-precision estimators. We
The increasing scale of machine learning models and data necessitates more efficient and robust estimation techniques for linear statistical models, pushing research in randomized algorithms.
This research offers a significant improvement in the efficiency and precision of estimating large-scale linear statistical models, a foundational component in many AI and data science applications.
The development of SLSE-FRS provides a new, more performant method for statistical estimation, potentially accelerating the training and deployment of complex AI systems by reducing computational burdens.
- · AI/ML researchers
- · Big data analytics companies
- · Developers of large-scale statistical models
- · Cloud computing providers
- · Organizations reliant on slower, less efficient estimation methods
Faster and more accurate training of large-scale AI models reliant on linear statistical estimations.
Reduced computational costs and energy consumption for certain machine learning tasks, potentially impacting compute infrastructure requirements.
Acceleration of research and development in areas that depend heavily on complex statistical modeling, leading to faster progress in various scientific and industrial applications.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG