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Research paper analyzes momentum's role in Polyak-Ruppert averaging

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This research paper explores the effectiveness of momentum in Polyak--Ruppert averaging for stochastic gradient descent (SGD) and related optimization methods. The authors analyze how quickly finite runs converge to asymptotic covariance and the potential for parallelization. For strongly convex quadratics with noise, they establish a bridge between empirical risk minimization and online procedures, detailing errors related to center, covariance, and shape. The paper also investigates the impact of heavy-tailed or state-dependent noise and clipping on these methods. AI

IMPACT Provides theoretical insights into optimization algorithms used in machine learning.

RANK_REASON The cluster contains a single academic paper published on arXiv. [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 →

Research paper analyzes momentum's role in Polyak-Ruppert averaging

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The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Alexandra Suvroikova, Darina Dvinskikh, Yuriy Dorn, Alexander Gasnikov ·

    When Momentum Helps Polyak--Ruppert Averaging

    arXiv:2610.27990v1 Announce Type: new Abstract: Polyak--Ruppert averaging gives SGD and momentum methods the same asymptotic covariance. We characterize how fast finite runs reach it and how far they can be parallelized. For strongly convex quadratics with additive noise, we prov…

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