论文标题
使用高斯工艺的vecchia近似值可扩展的贝叶斯优化
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes
论文作者
论文摘要
贝叶斯优化是一种用于优化黑框目标功能的技术。贝叶斯优化的核心是一个替代模型，可预测以前看不见的输入的目标函数的输出，以促进有希望的输入值的选择。高斯工艺（GPS）通常用作替代模型，但已知在观察次数的情况下缩放很差。我们适应了Vecchia近似，这是一种流行的GP近似值，从空间统计数据中启用可扩展的高维贝叶斯优化。我们开发了几种改进和扩展，包括使用迷你批次梯度下降，近似邻居搜索以及并行选择多个输入值。我们专注于通过Thompson采样来在信任区贝叶斯优化中使用扭曲的Vecchia GP。在几个测试功能和两个强化学习问题上，我们的方法与最新技术的状态进行了比较。
Bayesian optimization is a technique for optimizing black-box target functions. At the core of Bayesian optimization is a surrogate model that predicts the output of the target function at previously unseen inputs to facilitate the selection of promising input values. Gaussian processes (GPs) are commonly used as surrogate models but are known to scale poorly with the number of observations. We adapt the Vecchia approximation, a popular GP approximation from spatial statistics, to enable scalable high-dimensional Bayesian optimization. We develop several improvements and extensions, including training warped GPs using mini-batch gradient descent, approximate neighbor search, and selecting multiple input values in parallel. We focus on the use of our warped Vecchia GP in trust-region Bayesian optimization via Thompson sampling. On several test functions and on two reinforcement-learning problems, our methods compared favorably to the state of the art.
