Summary of Almost Sure Convergence Rates Of Stochastic Gradient Methods Under Gradient Domination, by Simon Weissmann et al.
Almost sure convergence rates of stochastic gradient methods under gradient domination
by Simon Weissmann, Sara Klein, Waïss Azizian, Leif Döring
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 Stochastic gradient methods are a cornerstone of machine learning, but their analysis is typically based on strong convexity, which rarely holds true in real-world applications. Recent work has shown that global and local gradient domination properties can be a more realistic assumption. This paper proves almost sure convergence rates for stochastic gradient descent (with and without momentum) under these assumptions, with rates that get arbitrarily close to recent expected rates. The results are demonstrated through applications in both supervised and reinforcement learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to train machine learning models. It shows that a type of algorithm called stochastic gradient descent can be used to find the best solution even when the problem is complex. This is important because many real-world problems don’t fit the simple assumptions that are usually made. The results can be applied to different types of learning, such as training neural networks or making decisions in complex situations. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning » Stochastic gradient descent » Supervised
