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新算法使用Hadamard和高斯池化加速\(\ell_2\)回归

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新的随机草图求解算法，用于加速超约束\(\ell_2\)回归。该算法引入了一种新颖的、快速的、稠密的随机变换，该变换结合了Hadamard展平、随机置换和高斯池化。这种新方法实现了\(\ell_\infty\)保证，具有\(m=O(\epsilon^{-2}d\log d)\)行，解决了先前工作中发现的独立性假设缺陷。 AI

影响 这项研究通过加速回归任务，可能导致更高效的机器学习模型训练。

排序理由 该集群包含两篇arXiv预印本，详细介绍了一种新的最小二乘回归算法。

在 Hugging Face Daily Papers 阅读 →

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新算法使用Hadamard和高斯池化加速\(\ell_2\)回归

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该集群包含两篇arXiv预印本，详细介绍了一种新的最小二乘回归算法。
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报道来源 [2]

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

    Hadamard 展平与高斯池化草图用于最小二乘法（逐坐标保证）

    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 展平与高斯池化草图用于最小二乘法及逐坐标保证

    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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