Summary of Accelerated Parameter-free Stochastic Optimization, by Itai Kreisler and Maor Ivgi and Oliver Hinder and Yair Carmon
Accelerated Parameter-Free Stochastic Optimization
by Itai Kreisler, Maor Ivgi, Oliver Hinder, Yair Carmon
First submitted to arxiv on: 31 Mar 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 The proposed method, U-DoG, achieves near-optimal rates for smooth stochastic convex optimization without requiring prior knowledge of problem parameters. Building upon UniXGrad and DoG methods, U-DoG combines novel iterate stabilization techniques to provide high probability guarantees under sub-Gaussian noise. The approach requires only loose bounds on the initial distance to optimality and noise magnitude. Experimental results demonstrate strong performance on convex problems and mixed outcomes for neural network training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary U-DoG is a new way to solve complex math problems without needing prior information about how close we are to the solution. This helps make it more efficient and reliable. The method combines ideas from previous approaches, UniXGrad and DoG, with some new tricks to keep the calculations stable. It can work well for many types of problems, but might not always be the best choice. |
Keywords
* Artificial intelligence * Neural network * Optimization * Probability
