Summary of Gradient Normalization Provably Benefits Nonconvex Sgd Under Heavy-tailed Noise, by Tao Sun et al.
Gradient Normalization Provably Benefits Nonconvex SGD under Heavy-Tailed Noise
by Tao Sun, Xinwang Liu, Kun Yuan
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper examines the roles of gradient normalization and clipping in ensuring the convergence of Stochastic Gradient Descent (SGD) when faced with heavy-tailed noise. Existing approaches consider gradient clipping essential for SGD convergence, but this study theoretically demonstrates that gradient normalization alone is sufficient to ensure convergence. The research also shows that combining gradient normalization with clipping leads to significantly improved convergence rates compared to using either technique individually, particularly as the noise decreases. The work provides theoretical evidence supporting the benefits of gradient normalization in SGD under heavy-tailed noise and introduces an accelerated SGD variant incorporating both techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make a computer learning algorithm called Stochastic Gradient Descent (SGD) work better when it’s dealing with noisy or unpredictable data. Some people thought that you needed to “clip” the gradients (which are like directions for the algorithm) to get good results, but this study shows that just “normalizing” them is enough. The researchers also found that combining both methods makes things even better. This helps us understand how SGD works and can be used in different situations. |
Keywords
* Artificial intelligence * Stochastic gradient descent
