Summary of Bayesian Optimization That Limits Search Region to Lower Dimensions Utilizing Local Gpr, by Yasunori Taguchi and Hiro Gangi
Bayesian Optimization that Limits Search Region to Lower Dimensions Utilizing Local GPR
by Yasunori Taguchi, Hiro Gangi
First submitted to arxiv on: 13 Mar 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 propose an innovative approach to Bayesian optimization (BO) that limits the search region to lower dimensions and utilizes local Gaussian process regression (LGPR) to scale the BO to higher dimensions. The proposed method aims to improve prediction accuracies and search efficiency in high-dimensional spaces while reducing computational costs. By treating the low-dimensional search region as “local,” LGPR achieves better prediction accuracy and reduces the time complexity of matrix inversion in the Gaussian process regression. Experimental results demonstrate that the proposed method outperforms existing methods on 20D Ackley and Rosenbrock functions, achieving search efficiencies equal to or higher than those of compared methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a special type of computer algorithm called Bayesian optimization (BO) to find the best settings for complex systems. The problem with BO is that it gets slower as the number of things being optimized increases. To fix this, the researchers developed a new way to use a tool called local Gaussian process regression (LGPR) that helps predict what will work well in different areas. They tested their method on some tricky math problems and found that it worked really well, beating other methods by a lot! They even used it to design a better power semiconductor device. |
Keywords
* Artificial intelligence * Optimization * Regression
