Summary of Score: a 1d Reparameterization Technique to Break Bayesian Optimization’s Curse Of Dimensionality, by Joseph Chakar
SCORE: A 1D Reparameterization Technique to Break Bayesian Optimization’s Curse of Dimensionality
by Joseph Chakar
First submitted to arxiv on: 18 Jun 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 Bayesian optimization has been a powerful tool for navigating complex search spaces, with applications in science and engineering. The method relies on a surrogate model to approximate the objective function, but this approach can lead to increased computational costs as the number of parameters and experiments grows. To address this issue, several methods have been proposed, including parallelization, surrogate model approximations, and memory pruning. However, these approaches all fall short of resolving the core issue behind BO’s curse of dimensionality. This paper proposes a 1D reparametrization trick to break this curse and sustain linear time complexity for BO in high-dimensional landscapes. The fast and scalable approach, called SCORE, can successfully find the global minimum of needle-in-a-haystack optimization functions and fit real-world data without requiring high-performance computing resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bayesian optimization is a powerful tool that helps scientists and engineers solve complex problems. In the past, it was used in areas like science and engineering to find the best solution among many options. However, as the number of options grows, the computer needs more time to do its job. To speed things up, some methods were developed, but they didn’t fully solve the problem. This new approach proposes a simple trick that makes Bayesian optimization faster and more efficient for complex problems. |
Keywords
» Artificial intelligence » Objective function » Optimization » Pruning
