Impactful applications such as materials discovery, hardware design, neural architecture search, or portfolio optimization require optimizing high-dimensional black-box functions with mixed and combinatorial input spaces. While Bayesian optimization has recently made significant progress in solving such problems, an in-depth analysis reveals that the current state-of-the-art methods are not reliable. Their performances degrade substantially when the unknown optima of the function do not have a certain structure. To fill the need for a reliable algorithm for combinatorial and mixed spaces, this paper proposes Bounce that relies on a novel map of various variable types into nested embeddings of increasing dimensionality. Comprehensive experiments show that Bounce reliably achieves and often even improves upon state-of-the-art performance on a variety of high-dimensional problems.

翻译：诸如材料发现、硬件设计、神经网络架构搜索或投资组合优化等具有影响力的应用，需要优化具有混合和组合输入空间的高维黑箱函数。尽管贝叶斯优化近期在解决此类问题上取得了显著进展，但深入分析表明当前最先进的方法并不可靠——当函数未知最优解不具备特定结构时，其性能会大幅下降。为满足组合与混合空间对可靠算法的需求，本文提出Bounce方法，其核心创新在于构建了一种将各类变量类型映射至维度递增的嵌套嵌入的新型映射机制。综合实验表明，Bounce能在各类高维问题上稳定达到甚至超越现有最优方法的性能表现。