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New research analyzes SGD convergence with noise and clipping · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Two new research papers explore the convergence properties of stochastic gradient descent (SGD) methods under challenging conditions. The first paper analyzes SGD with gradient clipping and additive Gaussian noise, proving almost sure convergence under smoothness and bounded noise assumptions. The second paper investigates SGD under heavy-tailed noise and Hölder smoothness, establishing new convergence rates for standard SGD, $\delta$-GClip, and G-Clip, including the first guarantee for a stochastic gradient method in a very heavy-tailed regime. AI

IMPACT These theoretical analyses could lead to more robust and efficient training methods for machine learning models, especially in scenarios with noisy data or complex objectives.

RANK_REASON Two academic papers published on arXiv detailing theoretical advancements in optimization algorithms for machine learning.

Read on arXiv cs.LG →

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

New research analyzes SGD convergence with noise and clipping · 2 sources tracked

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Amartya Mukherjee, Jun Liu ·

    Almost Sure Convergence Analysis of Stochastic Gradient Methods with Clipping and Additive Noise

    arXiv:2609.12119v1 Announce Type: new Abstract: Stochastic gradient descent (SGD) with gradient clipping and additive noise has become a standard technique for training machine learning models, particularly in applications requiring robustness or privacy guarantees. However, clip…

  2. arXiv cs.LG TIER_1 English(EN) · Misbah Uz Zaman, Anirbit Mukherjee ·

    Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise and H\"{o}lder Smoothness

    arXiv:2609.12785v1 Announce Type: new Abstract: Classical convergence guarantees for stochastic gradient methods typically assume Lipschitz-smooth objectives and finite-variance gradient noise, both frequently violated in practice. In contrast, we study nonconvex stochastic optim…

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