Summary of A Robbins–monro Sequence That Can Exploit Prior Information For Faster Convergence, by Siwei Liu and Ke Ma and Stephan M. Goetz
A Robbins–Monro Sequence That Can Exploit Prior Information For Faster Convergence
by Siwei Liu, Ke Ma, Stephan M. Goetz
First submitted to arxiv on: 6 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA); Optimization and Control (math.OC); Probability (math.PR); Statistics Theory (math.ST); Methodology (stat.ME); 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 proposed method enhances the convergence speed of the Robbins-Monro algorithm by incorporating prior information about the target point without relying on a potentially inaccurate regression model. This approach yields a convergent sequence for various prior distributions, including Gaussian and kernel density estimates. The results show that the modified sequence outperforms the standard one, especially in the early stages, which is crucial for applications with limited function measurements or noisy observations. The optimal parameters for this sequence are also proposed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper finds a new way to make an old algorithm work faster by using information we already know about what we’re looking for. This helps it get better results, especially when we don’t have much data or the data is messy. The method works with different kinds of prior information and performs well even when that information is not exactly right. |
Keywords
* Artificial intelligence * Regression
