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New paper offers strong Gaussian approximations for SGD

By PulseAugur Editorial · [1 sources] ·

This paper introduces strong Gaussian approximations for constant learning rate stochastic gradient descent (SGD), particularly for heavy-tailed noise and when the number of iterates grows while the learning rate shrinks. The research develops simple Gaussian processes and bounds the difference between SGD errors and their limits, enabling finite sample, whole-trajectory inference. The findings reveal a trade-off between controlling SGD bias and initialization effects, suggesting a burn-in scheme or Richardson-Romberg extrapolation to eliminate these contributions and achieve stochastic process convergence. AI

IMPACT Provides theoretical advancements in understanding the behavior of stochastic gradient descent, potentially leading to more robust optimization techniques in machine learning.

RANK_REASON Academic paper on statistical methods for machine learning. [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 paper offers strong Gaussian approximations for SGD

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Academic paper on statistical methods for machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Joseph Keslin, Wei Biao Wu ·

    Strong Gaussian Approximation of Constant-Learning-Rate SGD: Bias and Trajectory Inference

    arXiv:2610.14567v1 Announce Type: new Abstract: This paper develops strong Gaussian approximations (KMT type) for constant learning rate stochastic gradient descent (SGD). We work with heavy tailed noise and assume that the number of iterates grows and the learning rate shrinks s…

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