Summary of Practical Bayesian Algorithm Execution Via Posterior Sampling, by Chu Xin Cheng et al.
Practical Bayesian Algorithm Execution via Posterior Sampling
by Chu Xin Cheng, Raul Astudillo, Thomas Desautels, Yisong Yue
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); 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 The paper introduces Bayesian Algorithm Execution (BAX), a framework for selecting evaluation points of an expensive function to infer a property of interest. The authors propose a novel BAX method called PS-BAX, which uses posterior sampling to guide the selection process. This approach is simple, scalable, and competitive with existing baselines in various optimization and level set estimation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PS-BAX is a new way to select where to evaluate an expensive function to get useful information about something. It’s like guessing which points will give you the most important answers quickly. The authors tested PS-BAX on many different problems and it worked well, being faster and easier to use than other methods. |
Keywords
» Artificial intelligence » Optimization
