Sample-Efficient Bayesian Optimization with Transfer Learning for Heterogeneous Search Spaces
This work addresses a specific challenge in Bayesian optimization for researchers and practitioners dealing with limited data and varying parameter sets, representing an incremental advancement.
The paper tackles the problem of sample-efficient Bayesian optimization in settings with heterogeneous search spaces by proposing two transfer learning methods, which perform well on benchmark problems.
Bayesian optimization (BO) is a powerful approach to sample-efficient optimization of black-box functions. However, in settings with very few function evaluations, a successful application of BO may require transferring information from historical experiments. These related experiments may not have exactly the same tunable parameters (search spaces), motivating the need for BO with transfer learning for heterogeneous search spaces. In this paper, we propose two methods for this setting. The first approach leverages a Gaussian process (GP) model with a conditional kernel to transfer information between different search spaces. Our second approach treats the missing parameters as hyperparameters of the GP model that can be inferred jointly with the other GP hyperparameters or set to fixed values. We show that these two methods perform well on several benchmark problems.