论文标题
霉菌成图：高效的贝叶斯优化在混合空间上
Mold into a Graph: Efficient Bayesian Optimization over Mixed-Spaces
论文作者
论文摘要
现实世界中的优化问题通常不仅是黑框问题，而且还涉及分散和连续变量共存的输入类型的混合类型。这种混合空间优化具有对输入之间的复杂相互作用进行建模的主要挑战。在这项工作中，我们提出了一种新颖而简单的方法，需要利用图形数据结构来建模变量之间的基本关系，即变量为节点和由边缘定义的交互。然后，使用变异图自动编码器自然考虑了交互。我们首先提供了这种图形结构存在的经验证据，然后提出了图形结构学习和潜在空间优化的联合框架，以适应搜索最佳图形连接。实验结果表明，我们的方法表现出显着的性能，超过了许多合成和现实世界任务的现有方法，具有显着的计算效率。
Real-world optimization problems are generally not just black-box problems, but also involve mixed types of inputs in which discrete and continuous variables coexist. Such mixed-space optimization possesses the primary challenge of modeling complex interactions between the inputs. In this work, we propose a novel yet simple approach that entails exploiting the graph data structure to model the underlying relationship between variables, i.e., variables as nodes and interactions defined by edges. Then, a variational graph autoencoder is used to naturally take the interactions into account. We first provide empirical evidence of the existence of such graph structures and then suggest a joint framework of graph structure learning and latent space optimization to adaptively search for optimal graph connectivity. Experimental results demonstrate that our method shows remarkable performance, exceeding the existing approaches with significant computational efficiency for a number of synthetic and real-world tasks.
