Meta-Surrogate Benchmarking for Hyperparameter Optimization
This addresses the bottleneck for researchers and practitioners in HPO by providing a scalable benchmarking approach, though it is incremental as it builds on existing surrogate and multi-task modeling techniques.
The paper tackles the problem of expensive and limited hyperparameter optimization (HPO) benchmarks by proposing a meta-surrogate model that generates inexpensive, realistic tasks, enabling faster and more statistically significant comparisons of HPO methods.
Despite the recent progress in hyperparameter optimization (HPO), available benchmarks that resemble real-world scenarios consist of a few and very large problem instances that are expensive to solve. This blocks researchers and practitioners not only from systematically running large-scale comparisons that are needed to draw statistically significant results but also from reproducing experiments that were conducted before. This work proposes a method to alleviate these issues by means of a meta-surrogate model for HPO tasks trained on off-line generated data. The model combines a probabilistic encoder with a multi-task model such that it can generate inexpensive and realistic tasks of the class of problems of interest. We demonstrate that benchmarking HPO methods on samples of the generative model allows us to draw more coherent and statistically significant conclusions that can be reached orders of magnitude faster than using the original tasks. We provide evidence of our findings for various HPO methods on a wide class of problems.