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New arXiv papers detail advanced SGD optimization techniques · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Two new research papers published on arXiv explore advanced optimization techniques for stochastic gradient descent (SGD). The first paper, "Sharp Stationary Gaussian Approximation for Constant-Stepsize SGD," by Junghoon Seo, provides a theoretical framework for approximating SGD's invariant law with a Gaussian distribution under specific conditions. The second paper, "High-Probability Convergence of Clipped SGD under Heavy-Tailed Noise and (L0,L1)-Smoothness," by Eduard Gorbunov, addresses the convergence challenges of clipped SGD when dealing with heavy-tailed noise, offering improved high-probability guarantees for convex objectives. AI

IMPACT These papers offer theoretical advancements in optimization algorithms, potentially improving the efficiency and robustness of training large machine learning models.

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 arXiv papers detail advanced SGD optimization techniques · 2 sources tracked

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Junghoon Seo ·

    Sharp Stationary Gaussian Approximation for Constant-Stepsize SGD

    arXiv:2609.39144v2 Announce Type: new Abstract: We prove a sharp Gaussian approximation for the invariant law of constant-stepsize SGD with bounded additive noise generated by an exogenous uniformly ergodic Markov chain. For a smooth, strongly convex objective with a Lipschitz He…

  2. arXiv cs.LG TIER_1 English(EN) · Taha El Bakkali El Kadi, Savelii Chezhegov, Aleksandr Beznosikov, Samuel Horv\'ath, Eduard Gorbunov ·

    High-Probability Convergence of Clipped SGD under Heavy-Tailed Noise and $(L_0,L_1)$-Smoothness

    arXiv:2505.20817v3 Announce Type: replace-cross Abstract: Gradient clipping is widely used in language-model training to control heavy-tailed gradient noise and can improve convergence guarantees over stochastic gradient descent (SGD) under $(L_0,L_1)$-smoothness. Under these joi…

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