Automating Bayesian optimization with Bayesian optimization
Automating Bayesian optimization with Bayesian optimization
复制标题
使用贝叶斯优化自动化贝叶斯优化
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
R. Garnett
中科院分区：
文献类型：
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作者：
Gustavo Malkomes;R. Garnett
贝叶斯优化是求解昂贵函数全局优化问题的有力工具。其关键组成部分之一是用于目标函数f的潜在概率模型。然而，在实践中，人们往往不清楚如何适当地选择一个模型，特别是当收集数据是昂贵的。在这项工作中，我们介绍了一种新的自动贝叶斯优化方法，动态选择有前途的模型来解释观察到的数据，使用贝叶斯优化模型空间。至关重要的是，我们考虑了模型选择的不确定性;我们的方法能够使用多个模型来表示其当前对f的信念，并随后使用这些信息进行决策。我们认为，并证明经验，我们的方法自动找到合适的模型的目标函数，这最终导致更有效的优化。
Bayesian optimization is a powerful tool for global optimization of expensive functions. One of its key components is the underlying probabilistic model used for the objective function f. In practice, however, it is often unclear how one should appropriately choose a model, especially when gathering data is expensive. In this work, we introduce a novel automated Bayesian optimization approach that dynamically selects promising models for explaining the observed data using Bayesian Optimization in the model space. Crucially, we account for the uncertainty in the choice of model; our method is capable of using multiple models to represent its current belief about f and subsequently using this information for decision making. We argue, and demonstrate empirically, that our approach automatically finds suitable models for the objective function, which ultimately results in more-efficient optimization.