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New arXiv Paper Analyzes SGD with Infinite-Variance Gradients

By PulseAugur Editorial · [1 sources] ·

A new arXiv paper delves into the mathematical underpinnings of Stochastic Gradient Descent (SGD) when dealing with infinite-variance gradient noise. The research characterizes SGD's accuracy by analyzing exact first-order tail probabilities for the last iterate of nonlinear, constant-stepsize SGD. It demonstrates that for specific gradient noise characteristics, SGD can exhibit infinite expected excess objective even when most iterations are near the optimum, providing a detailed mathematical framework for understanding these extreme behaviors. AI

IMPACT Provides a deeper theoretical understanding of optimization algorithms crucial for training large AI models.

RANK_REASON The cluster contains a single academic paper published on arXiv detailing theoretical mathematical analysis of an optimization algorithm. [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 arXiv Paper Analyzes SGD with Infinite-Variance Gradients

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The cluster contains a single academic paper published on arXiv detailing theoretical mathematical analysis of an optimization algorithm. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Xiaoli Li, Qianqian Lei, Ziyang Wei, Wei Biao Wu ·

    Exact Tail Laws for Nonlinear SGD with Infinite-Variance Gradients

    arXiv:2610.23750v1 Announce Type: cross Abstract: With infinite-variance gradient noise, SGD can have infinite expected excess objective even when most iterates are close to the optimum. We characterize its accuracy through exact first-order tail probabilities for the last iterat…

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