Summary of On Regularization Via Early Stopping For Least Squares Regression, by Rishi Sonthalia and Jackie Lok and Elizaveta Rebrova
On Regularization via Early Stopping for Least Squares Regression
by Rishi Sonthalia, Jackie Lok, Elizaveta Rebrova
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); Statistics Theory (math.ST); 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 investigates the impact of early stopping on machine learning models, specifically linear regression, using discrete full batch gradient descent. It characterizes the trajectory of parameters and expected excess risk with minimal assumptions. The authors show that early stopped solutions are equivalent to minimum norm solutions for generalized ridge regularized problems, and prove that early stopping is beneficial for generic data with arbitrary spectrum and various learning rate schedules. They also provide an estimate for optimal stopping time and empirically validate it. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how “stopping early” affects the results of machine learning models, like linear regression. It’s trying to figure out what happens when you stop training too soon, and if that’s good or bad. The authors use a specific way of updating the model (discrete full batch gradient descent) and show that stopping early is actually helpful for many types of data and ways of adjusting the learning rate. |
Keywords
* Artificial intelligence * Early stopping * Gradient descent * Linear regression * Machine learning
