Optimizing discrete black-box functions is key in several domains, e.g.
protein engineering and drug design. Due to the lack of gradient information
and the need for sample efficiency, Bayesian optimization is an ideal candidate
for these tasks. Several methods for high-dimensional continuous and
categorical Bayesian optimization have been proposed recently. However, our
survey of the field reveals highly heterogeneous experimental set-ups across
methods and technical barriers for the replicability and application of
published algorithms to real-world tasks. To address these issues, we develop a
unified framework to test a vast array of high-dimensional Bayesian
optimization methods and a collection of standardized black-box functions
representing real-world application domains in chemistry and biology. These two
components of the benchmark are each supported by flexible, scalable, and
easily extendable software libraries (poli and poli-baselines), allowing
practitioners to readily incorporate new optimization objectives or discrete
optimizers. Project website:
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通过统一的框架和标准化的黑盒函数，针对化学和生物学的实际应用领域，该论文研究了贝叶斯优化的高维优化方法和技术难点，并提供了易于拓展的软件库，以方便实践者更好地应用于离散优化问题。