  With the surge in the number of hyperparameters and training times of modern
machine learning models, hyperparameter tuning is becoming increasingly
expensive. However, after assessing 40 tuning methods systematically, we find
that each faces certain limitations. In particular, methods that speed up
tuning via knowledge transfer typically require the final performance of
hyperparameters and do not focus on low-fidelity information. As we demonstrate
empirically, this common practice is suboptimal and can incur an unnecessary
use of resources. It is more cost-efficient to instead leverage low-fidelity
tuning observations to measure inter-task similarity and transfer knowledge
from existing to new tasks accordingly. However, performing multi-fidelity
tuning comes with its own challenges in the transfer setting: the noise in
additional observations and the need for performance forecasting. Therefore, we
propose and conduct a thorough analysis of a multi-task multi-fidelity Bayesian
optimization framework, which leads to the best instantiation--amortized
auto-tuning (AT2). We further present an offline-computed 27-task
hyperparameter recommendation (HyperRec) database to serve the community.
Extensive experiments on HyperRec and other real-world databases illustrate the
effectiveness of our AT2 method.
