Summary of Vanilla Bayesian Optimization Performs Great in High Dimensions, by Carl Hvarfner and Erik Orm Hellsten and Luigi Nardi
Vanilla Bayesian Optimization Performs Great in High Dimensions
by Carl Hvarfner, Erik Orm Hellsten, Luigi Nardi
First submitted to arxiv on: 3 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 This paper addresses the limitations of Bayesian optimization algorithms in high-dimensional problems. By analyzing the degeneracies that make vanilla Bayesian optimization poorly suited to these tasks, researchers identify ways to reduce complexity without imposing structural restrictions on the objective. A proposed enhancement to Gaussian process lengthscale prior scaling is shown to significantly improve performance in high-dimensional settings, outperforming existing state-of-the-art algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bayesian optimization tries to find the best solution by guessing and learning from mistakes. But what happens when we have lots of variables? This paper shows that traditional methods don’t work well in these situations because they get stuck or slow down. The authors suggest a simple fix: adjust the way we think about how complex our solutions are based on the number of variables. This makes Bayesian optimization perform much better than other methods. |
Keywords
* Artificial intelligence * Optimization
