Summary of Stopping Bayesian Optimization with Probabilistic Regret Bounds, by James T. Wilson
Stopping Bayesian Optimization with Probabilistic Regret Bounds
by James T. Wilson
First submitted to arxiv on: 26 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 is a widely used framework for tackling complex search problems. A typical Bayesian optimization algorithm iteratively selects what to evaluate next until it exhausts its budget. This paper explores an alternative stopping criterion based on the probability that a solution satisfies specific conditions. Specifically, we focus on the (ε, δ)-criterion, which stops when a solution is found within ε of the optimum with at least 1 – δ probability under the model. We show that Bayesian optimization satisfies this criterion for Gaussian process priors under mild technical assumptions and provide a practical algorithm to evaluate Monte Carlo stopping rules efficiently and robustly to estimation errors. Our findings are supported by empirical results demonstrating the strengths and limitations of our approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding the best solution in complex problems. Normally, computers stop searching when they’ve used up their allowed time or budget. The researchers looked at a different way to decide when to stop: using probability. They tested this idea on a common computer problem called Bayesian optimization and showed that it works well with certain types of data. They also gave a new method for computers to quickly and accurately decide when to stop searching. |
Keywords
* Artificial intelligence * Optimization * Probability
