Summary of Beyond Regrets: Geometric Metrics For Bayesian Optimization, by Jungtaek Kim
Beyond Regrets: Geometric Metrics for Bayesian Optimization
by Jungtaek Kim
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new approach to evaluating Bayesian optimization is proposed, addressing limitations in current regret-based metrics. The traditional metrics only consider function evaluations and cannot discriminate between finding multiple global solutions or exploiting/exploiting a search space. To overcome these issues, four geometric metrics are introduced: precision, recall, average degree, and average distance. These metrics allow for comparison of Bayesian optimization algorithms considering query points and global optima, but require careful selection of an additional parameter. To alleviate this issue, parameter-free forms are derived by integrating out the extra parameter. The proposed metrics provide a more nuanced understanding of Bayesian optimization performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bayesian optimization is a way to find the best option when we don’t know what makes something good or bad. Right now, it’s hard to measure how well this approach works because current methods only look at how many times we evaluate the function. This doesn’t tell us if we’re finding multiple good options or just one really great one. To fix this problem, scientists have come up with four new ways to measure success: precision, recall, average degree, and average distance. These metrics help us see if Bayesian optimization is doing a good job of searching for the best option, but they require some extra work to set them up. By making these metrics easier to use, we can better understand how well Bayesian optimization works. |
Keywords
* Artificial intelligence * Optimization * Precision * Recall
