Sample-Efficient Bayesian Optimization with Transfer Learning for Heterogeneous Search Spaces

Aryan Deshwal, Sait Cakmak, Yuhou Xia, David Eriksson

Keywords: Bayesian optimization

Abstract: 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.

Submission Checklist: Yes

Broader Impact Statement: Yes

Paper Availability And License: Yes

Code Of Conduct: Yes

Optional Meta-Data For Green-AutoML: All questions below on environmental impact are optional.

Steps For Environmental Footprint Reduction During Development: We tried our best to minimize the amount of compute used while still obtaining statistically significant results

CPU Hours: 156

GPU Hours: 0

TPU Hours: 0

Evaluation Metrics: No

Submission Number: 7