Summary of Convex Sgd: Generalization Without Early Stopping, by Julien Hendrickx and Alex Olshevsky
Convex SGD: Generalization Without Early Stopping
by Julien Hendrickx, Alex Olshevsky
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST)
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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 This paper explores the generalization error of stochastic gradient descent (SGD) on smooth convex functions. The authors demonstrate a new bound that vanishes as the number of iterations (T) and dataset size (n) approach zero, scaling with T and n as O(1/√T + 1/√n). Crucially, this performance is achieved without requiring strong convexity. The proposed SGD algorithm uses a step-size schedule of αt = 1/√t, which enables generalization well. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Stochastic gradient descent is an important technique in machine learning that helps us make predictions on new data. In this paper, scientists study how well this method works when it’s used to optimize smooth and convex functions. They show that the algorithm can generalize well even without some strong assumptions, which is a significant finding. |
Keywords
* Artificial intelligence * Generalization * Machine learning * Stochastic gradient descent
