论文标题
可扩展的贝叶斯优化，稀疏高斯工艺模型
Scalable Bayesian Optimization with Sparse Gaussian Process Models
论文作者
论文摘要
本文的重点是贝叶斯优化，其改进来自两个方面：（i）使用衍生信息来加速优化收敛； （ii）考虑可扩展的GP来处理大量数据。
This thesis focuses on Bayesian optimization with the improvements coming from two aspects:(i) the use of derivative information to accelerate the optimization convergence; and (ii) the consideration of scalable GPs for handling massive data.
