  Hyperparameters of Deep Learning (DL) pipelines are crucial for their
downstream performance. While a large number of methods for Hyperparameter
Optimization (HPO) have been developed, their incurred costs are often
untenable for modern DL. Consequently, manual experimentation is still the most
prevalent approach to optimize hyperparameters, relying on the researcher's
intuition, domain knowledge, and cheap preliminary explorations. To resolve
this misalignment between HPO algorithms and DL researchers, we propose
PriorBand, an HPO algorithm tailored to DL, able to utilize both expert beliefs
and cheap proxy tasks. Empirically, we demonstrate PriorBand's efficiency
across a range of DL benchmarks and show its gains under informative expert
input and robustness against poor expert beliefs
