论文标题
结构内核通过贝叶斯优化和象征性最佳运输
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport
论文作者
论文摘要
尽管自动化机器学习最近取得了进步，但模型选择仍然是一个复杂且计算密集的过程。对于高斯流程（GPS），选择内核是一项至关重要的任务，通常是由专家手动完成的。此外，评估高斯过程的模型选择标准通常在样本量中立方扩展，从而使内核搜索在计算上特别昂贵。我们通过一般结构化的内核空间提出了一种新颖，有效的搜索方法。先前的方法通过贝叶斯优化解决了此任务，并依赖于直接在功能空间中的GP之间的距离来构建内核 - 内核。我们通过定义与内核相关的统计假设的符号表示，提出了一种替代方法。我们从经验上表明，这导致了通过离散内核空间进行搜索的计算更有效的方法。
Despite recent advances in automated machine learning, model selection is still a complex and computationally intensive process. For Gaussian processes (GPs), selecting the kernel is a crucial task, often done manually by the expert. Additionally, evaluating the model selection criteria for Gaussian processes typically scales cubically in the sample size, rendering kernel search particularly computationally expensive. We propose a novel, efficient search method through a general, structured kernel space. Previous methods solved this task via Bayesian optimization and relied on measuring the distance between GP's directly in function space to construct a kernel-kernel. We present an alternative approach by defining a kernel-kernel over the symbolic representation of the statistical hypothesis that is associated with a kernel. We empirically show that this leads to a computationally more efficient way of searching through a discrete kernel space.
