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Bayesian optimization (BO) is a sample-efficient global optimizationalgorithm for black-box functions which are expensive to evaluate. Existingliterature on model based optimization in conditional parameter spaces areusually built on trees. In this work, we generalize the additive assumption totree-structured functions and propose an additive tree-structured covariancefunction, showing improved sample-efficiency, wider applicability and greaterflexibility. Furthermore, by incorporating the structure information ofparameter spaces and the additive assumption in the BO loop, we develop aparallel algorithm to optimize the acquisition function and this optimizationcan be performed in a low dimensional space. We demonstrate our method on anoptimization benchmark function, as well as on a neural network modelcompression problem, and experimental results show our approach significantlyoutperforms the current state of the art for conditional parameter optimizationincluding SMAC, TPE and Jenatton et al. (2017).

Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization