محفوظ في:
| المؤلفون الرئيسيون: | , , , , |
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| التنسيق: | Preprint |
| منشور في: |
2020
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://arxiv.org/abs/2009.02539 |
| الوسوم: |
إضافة وسم
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جدول المحتويات:
- Bayesian optimisation is a popular method for efficient optimisation of expensive black-box functions. Traditionally, BO assumes that the search space is known. However, in many problems, this assumption does not hold. To this end, we propose a novel BO algorithm which expands (and shifts) the search space over iterations based on controlling the expansion rate thought a hyperharmonic series. Further, we propose another variant of our algorithm that scales to high dimensions. We show theoretically that for both our algorithms, the cumulative regret grows at sub-linear rates. Our experiments with synthetic and real-world optimisation tasks demonstrate the superiority of our algorithms over the current state-of-the-art methods for Bayesian optimisation in unknown search space.