Summary of Cost-aware Bayesian Optimization Via the Pandora’s Box Gittins Index, by Qian Xie and Raul Astudillo and Peter I. Frazier and Ziv Scully and Alexander Terenin
Cost-aware Bayesian Optimization via the Pandora’s Box Gittins Index
by Qian Xie, Raul Astudillo, Peter I. Frazier, Ziv Scully, Alexander Terenin
First submitted to arxiv on: 28 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 is a technique for efficiently optimizing unknown functions with limited resources. To incorporate function evaluation costs, we develop a connection between cost-aware Bayesian optimization and the Pandora’s Box problem. We show that an acquisition function called the Gittins index can be used to optimize unknown functions while considering costs. Our results demonstrate empirically that this approach performs well in medium-high dimensions. Additionally, we find that this performance carries over to classical Bayesian optimization without explicit evaluation costs. Our work combines techniques from Gittins index theory with Bayesian optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bayesian optimization helps us find the best solution for a problem when we don’t know the rules of the game. It’s like trying to find the perfect combination of ingredients for your favorite recipe, but you can only test one ingredient at a time and it costs something to do each test. To make this process more efficient, we looked at a famous economics problem called Pandora’s Box and found that its solution can be used as a guide for our optimization method. We tested this approach and found that it works well in certain situations. This research is an important step towards using these techniques together. |
Keywords
» Artificial intelligence » Optimization
