Summary of Poisson Process For Bayesian Optimization, by Xiaoxing Wang et al.
Poisson Process for Bayesian Optimization
by Xiaoxing Wang, Jiaxing Li, Chao Xue, Wei Liu, Weifeng Liu, Xiaokang Yang, Junchi Yan, Dacheng Tao
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel Bayesian Optimization framework, Poisson Process Bayesian Optimization (PoPBO), is proposed to efficiently optimize black-box functions while estimating relative rankings of candidates. The framework leverages a probabilistic surrogate model based on the Poisson process and incorporates tailored acquisition functions derived from LCB and EI methods. Compared to classic GP-BO, PoPBO demonstrates lower computation costs and improved robustness to noise in experiments on simulated and real-world benchmarks for hyperparameter optimization (HPO) and neural architecture search (NAS). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a new way to find the best option from a group of possibilities. They used something called the Poisson process, which is like a special kind of math problem that helps them figure out how good each option is relative to the others. This method is better than previous methods because it’s faster and more resistant to mistakes. The team tested their idea on some computer programs and found that it worked really well. |
Keywords
* Artificial intelligence * Hyperparameter * Optimization
