Summary of High-dimensional Bayesian Optimization Via Covariance Matrix Adaptation Strategy, by Lam Ngo et al.
High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy
by Lam Ngo, Huong Ha, Jeffrey Chan, Vu Nguyen, Hongyu Zhang
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 Bayesian Optimization (BO) is a widely used method for finding the global optimum of expensive black-box functions, but its application to high-dimensional optimization problems can be challenging. To overcome this limitation, researchers have proposed using local search strategies that partition the search domain into regions with high likelihood of containing the global optimum. The paper proposes a novel technique called Covariance Matrix Adaptation (CMA) that learns a search distribution to estimate the probabilities of data points being the global optimum. This search distribution is then used to define local regions, which can be optimized using existing BO optimizers like BO, TuRBO, and BAxUS. The paper demonstrates the effectiveness of this approach on various benchmark synthetic and real-world problems, outperforming state-of-the-art techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bayesian Optimization (BO) is a way to find the best answer for a complicated problem where we don’t know how it works inside. But when we have many variables to consider, BO gets tricky. One idea is to split the search area into smaller parts that are more likely to contain the best answer. The paper introduces a new method called Covariance Matrix Adaptation (CMA) that helps us find these “good” areas by looking at how data points are connected. We can then use existing BO optimizers in these areas to find the best answer. The results show that this approach is better than what others have done before. |
Keywords
* Artificial intelligence * Likelihood * Optimization
