Summary of Standard Gaussian Process Is All You Need For High-dimensional Bayesian Optimization, by Zhitong Xu et al.
Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization
by Zhitong Xu, Haitao Wang, Jeff M Phillips, Shandian Zhe
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 In this paper, researchers investigate the performance of Bayesian Optimization (BO) with Gaussian processes (GP) in high-dimensional optimization problems. The common perception is that standard BO underperforms in these scenarios, but the study finds that using Matérn kernels instead of Square Exponential (SE) kernels can lead to top-tier results, often surpassing methods specifically designed for high-dimensional optimization. The paper also provides theoretical analysis and proposes a simple robust initialization strategy to improve the performance of SE kernel. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bayesian Optimization is a machine learning technique that uses Gaussian processes to find the best parameters for a given model. In this study, researchers looked at how well BO works in high-dimensional problems, where there are many variables to consider. They found that using Matérn kernels instead of Square Exponential (SE) kernels can make BO work better. |
Keywords
* Artificial intelligence * Machine learning * Optimization
