Sketching in Regression: Sketchy in Practice?

The Johnson-Lindenstrauss (JL) lemma and its extensions provide tools for solving the $L_2$ regression problem in high-dimensional settings with $n>d$, by transforming a subspace of $\mathbb{R}^{n}$ to a subspace of $\mathbb{R}^{m}$, (where $m

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Publication Details

Journal
arXiv (Cornell University)
Published
2026-11-02
DOI
https://doi.org/10.48550/arxiv.2611.02808
Primary Topic
Stochastic Gradient Optimization Techniques
Type
preprint
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preprint

Sketching in Regression: Sketchy in Practice?

Koustav Goswami, Soupayan Das Gupta, Somarddha Das
arXiv (Cornell University)
Stochastic Gradient Optimization Techniques
preprint

Sketching in Regression: Sketchy in Practice?

Koustav Goswami, Soupayan Das Gupta, Somarddha Das
preprint en

Abstract

The Johnson-Lindenstrauss (JL) lemma and its extensions provide tools for solving the $L_2$ regression problem in high-dimensional settings with $n>d$, by transforming a subspace of $\mathbb{R}^{n}$ to a subspace of $\mathbb{R}^{m}$, (where $m

arXiv (Cornell University)
Indian Statistical Institute (IN)
Stochastic Gradient Optimization Techniques
DOIOpen Access PDFOpenAlex
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Sketching in Regression: Sketchy in Practice? — Koustav Goswami, Soupayan Das Gupta, et al. · arXiv (Cornell University) (2026) | TGRS Research Map | TGRS