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New risk bounds for tail-averaged SGD in linear regression

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This paper introduces a new theoretical framework for analyzing the performance of Stochastic Gradient Descent (SGD) with tail-averaging in high-dimensional linear regression. The authors establish matching upper and lower bounds on the excess risk, refining existing analyses by accounting for the impact of initialization on spectral properties. They demonstrate that the initial error's influence is exponentially damped during the burn-in phase, and they identify specific stepsize regimes where their theoretical conditions are met. AI

IMPACT Refines theoretical understanding of optimization algorithms, potentially leading to more efficient training of machine learning models.

RANK_REASON The item is an academic paper detailing theoretical advancements in machine learning algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New risk bounds for tail-averaged SGD in linear regression

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The item is an academic paper detailing theoretical advancements in machine learning algorithms. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Qingyue Zhao, Xuheng Li, Quanquan Gu ·

    Settling the Burn-in Gap: Sharp Risk Bounds for Tail-Averaged SGD in Linear Regression

    arXiv:2611.13383v1 Announce Type: new Abstract: We study the excess risk of constant-stepsize online stochastic gradient descent (SGD) with tail-averaging for high-dimensional linear regression. Building on the instance-dependent spectral framework of Zou et al. (2021), we establ…

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