计算机科学
文件夹
数学优化
投资组合优化
贝叶斯概率
贝叶斯优化
项目组合管理
资产配置
作者
Matthew W. Hoffman,Eric Brochu,Nando de Freitas
出处
期刊:Uncertainty in Artificial Intelligence
日期:2011-07-14
卷期号:: 327-336
被引量:136
摘要
Bayesian optimization with Gaussian processes has become an increasingly popular tool in the machine learning community. It is efficient and can be used when very little is known about the objective function, making it popular in expensive black-box optimization scenarios. It uses Bayesian methods to sample the objective efficiently using an acquisition function which incorporates the posterior estimate of the objective. However, there are several different parameterized acquisition functions in the literature, and it is often unclear which one to use. Instead of using a single acquisition function, we adopt a portfolio of acquisition functions governed by an online multi-armed bandit strategy. We propose several portfolio strategies, the best of which we call GP-Hedge, and show that this method outperforms the best individual acquisition function. We also provide a theoretical bound on the algorithm's performance.
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