Vanilla SGD with Momentum Survives Heavy-Tailed Noise: Convergence Analysis without Gradient Clipping or Normalization
arXiv:2607.08104v1 Announce Type: new Abstract: Stochastic gradient descent (SGD) is a cornerstone of modern optimization. While its performance under heavy-tailed noise is often addressed through specialized modifications such as gradient clipping or normalization, we investigate a more fundamental question: how does vanilla SGD, particularly with momentum, perform in the presence of heavy-tailed noise? In this paper, we refine existing convergence results for vanilla SGD and, more importantly, provide the first comprehensive convergence analysis of vanilla SGD with momentum for strongly conv
This research refines our understanding of core machine learning optimization techniques, specifically addressing the robustness of SGD with momentum to real-world data imperfections.
Improved theoretical understanding of vanilla SGD's resilience under heavy-tailed noise can lead to more stable and efficient AI model training without complex workarounds.
The perceived necessity of gradient clipping or normalization for robust training with heavy-tailed data may diminish, simplifying model development and deployment.
- · AI/ML researchers
- · AI model developers
- · Deep learning frameworks
- · Developers of specialized heavy-tailed noise handling techniques
More reliable training of deep learning models on noisy or adversarial datasets without additional complexity.
Faster iteration cycles in AI research and development due to simpler and more robust optimization algorithms.
Potentially enables new applications for AI in domains previously limited by data quality and noise issues.
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Read at arXiv cs.LG