Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization
This work addresses the challenge of efficient optimization in conditional parameter spaces, which is incremental but offers practical benefits for applications like model compression.
The paper tackles the problem of optimizing black-box functions in conditional parameter spaces by proposing an additive tree-structured covariance function for Bayesian optimization, resulting in improved sample efficiency and outperforming state-of-the-art methods like SMAC, TPE, and Jenatton et al. (2017) on benchmark and neural network compression tasks.
Bayesian optimization (BO) is a sample-efficient global optimization algorithm for black-box functions which are expensive to evaluate. Existing literature on model based optimization in conditional parameter spaces are usually built on trees. In this work, we generalize the additive assumption to tree-structured functions and propose an additive tree-structured covariance function, showing improved sample-efficiency, wider applicability and greater flexibility. Furthermore, by incorporating the structure information of parameter spaces and the additive assumption in the BO loop, we develop a parallel algorithm to optimize the acquisition function and this optimization can be performed in a low dimensional space. We demonstrate our method on an optimization benchmark function, as well as on a neural network model compression problem, and experimental results show our approach significantly outperforms the current state of the art for conditional parameter optimization including SMAC, TPE and Jenatton et al. (2017).