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New algorithm accelerates \(\ell_2\) regression with Hadamard and Gaussian pooling

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new randomized sketch-and-solve algorithm to accelerate overconstrained \(\ell_2\) regression. The algorithm introduces a novel fast, dense randomized transform that combines Hadamard flattening, a random permutation, and Gaussian pooling. This new method achieves an \(\ell_\infty\) guarantee with \(m=O(\epsilon^{-2}d\log d)\) rows, addressing a previously identified independence assumption flaw in prior work. AI

IMPACT This research could lead to more efficient machine learning model training by accelerating regression tasks.

RANK_REASON The cluster contains two arXiv preprints detailing a new algorithm for least squares regression.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New algorithm accelerates \(\ell_2\) regression with Hadamard and Gaussian pooling

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The cluster contains two arXiv preprints detailing a new algorithm for least squares regression.
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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Hadamard Flattening and Gaussian Pooling Sketch for Least Squares with Coordinate-wise Guarantee

    Randomized sketch-and-solve algorithms accelerate overconstrained $\ell_2$ regression by replacing the input with a smaller problem. Standard subspace embeddings guarantee that the cost of the regression is nearly preserved, but coordinate-wise accuracy of the solution is more de…

  2. arXiv stat.ML TIER_1 English(EN) · Zhao Song, Lichen Zhang ·

    Hadamard Flattening and Gaussian Pooling Sketch for Least Squares with Coordinate-wise Guarantee

    arXiv:2608.26552v1 Announce Type: cross Abstract: Randomized sketch-and-solve algorithms accelerate overconstrained $\ell_2$ regression by replacing the input with a smaller problem. Standard subspace embeddings guarantee that the cost of the regression is nearly preserved, but c…

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