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New research identifies stabilization threshold for dynamic preconditioning in online inference

By PulseAugur Editorial · [2 sources] ·

Researchers have identified a critical stabilization threshold for dynamic preconditioning in gradient descent methods. This threshold determines when the Polyak-Ruppert averaging technique, fundamental for online inference, maintains its asymptotic normality. The study proposes a preconditioner-isolating decomposition to analyze the error dynamics and establishes that the rate at which the preconditioning matrix stabilizes must exceed a specific threshold related to the step-size exponent. AI

IMPACT Provides theoretical underpinnings for the stability of advanced optimization techniques used in training large-scale machine learning models.

RANK_REASON Academic paper published on arXiv detailing theoretical advancements in optimization algorithms.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research identifies stabilization threshold for dynamic preconditioning in online inference

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Sunyoung An, Xiaoming Huo ·

    When Does Dynamic Preconditioning Preserve the Polyak-Ruppert CLT? A Stabilization Threshold

    arXiv:2604.23498v1 Announce Type: cross Abstract: Polyak-Ruppert averaging yields an asymptotically normal estimator with sandwich covariance $H^{-1}SH^{-1}$, the foundation of online inference. When the gradient step is preconditioned by a data-driven matrix $P_t$, we ask how fa…

  2. arXiv stat.ML TIER_1 English(EN) · Xiaoming Huo ·

    When Does Dynamic Preconditioning Preserve the Polyak-Ruppert CLT? A Stabilization Threshold

    Polyak-Ruppert averaging yields an asymptotically normal estimator with sandwich covariance $H^{-1}SH^{-1}$, the foundation of online inference. When the gradient step is preconditioned by a data-driven matrix $P_t$, we ask how fast $P_t$ must stabilize for the central limit theo…

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