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New research details high-order error bounds for Markovian LSA algorithms

By PulseAugur Editorial · [1 sources] ·

A new paper on arXiv details advancements in Linear Stochastic Approximation (LSA) algorithms, specifically addressing bias and error bounds under Markovian noise. The research introduces a novel bias decomposition technique and applies Richardson-Romberg extrapolation to cancel the leading bias term. The findings suggest that the error bounds for the extrapolated iterates can match the optimal covariance matrix of standard averaged LSA. AI

IMPACT This research could lead to more accurate and efficient optimization algorithms for machine learning models.

RANK_REASON The cluster contains an academic paper detailing a novel algorithm and theoretical findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

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

New research details high-order error bounds for Markovian LSA algorithms

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The cluster contains an academic paper detailing a novel algorithm and theoretical findings. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Ilya Levin, Alexey Naumov, Sergey Samsonov ·

    High-Order Error Bounds for Markovian LSA with Richardson-Romberg Extrapolation

    arXiv:2508.05570v2 Announce Type: replace Abstract: In this paper, we study the bias and high-order error bounds of the Linear Stochastic Approximation (LSA) algorithm with Polyak-Ruppert (PR) averaging under Markovian noise. We focus on the version of the algorithm with constant…

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