تقرير
Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation
| العنوان: | Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation |
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| المؤلفون: | Zhang, Fengxue, Song, Jialin, Bowden, James, Ladd, Alexander, Yue, Yisong, Desautels, Thomas A., Chen, Yuxin |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | We study Bayesian optimization (BO) in high-dimensional and non-stationary scenarios. Existing algorithms for such scenarios typically require extensive hyperparameter tuning, which limits their practical effectiveness. We propose a framework, called BALLET, which adaptively filters for a high-confidence region of interest (ROI) as a superlevel-set of a nonparametric probabilistic model such as a Gaussian process (GP). Our approach is easy to tune, and is able to focus on local region of the optimization space that can be tackled by existing BO methods. The key idea is to use two probabilistic models: a coarse GP to identify the ROI, and a localized GP for optimization within the ROI. We show theoretically that BALLET can efficiently shrink the search space, and can exhibit a tighter regret bound than standard BO without ROI filtering. We demonstrate empirically the effectiveness of BALLET on both synthetic and real-world optimization tasks. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2307.13371 |
| رقم الأكسشن: | edsarx.2307.13371 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |