While Deep Learning (DL) experts often have prior knowledge about which hyperparameter settings yield strong performance, only few Hyperparameter Optimization (HPO) algorithms can leverage such prior knowledge and none incorporate priors over multiple objectives. As DL practitioners often need to optimize not just one but many objectives, this is a blind spot in the algorithmic landscape of HPO. To address this shortcoming, we propose the novel problem setting of multi-objective HPO with expert priors and introduce PriMO, the first HPO algorithm that can integrate user beliefs over multiple objectives. We show PriMO achieves state-of-the-art performance across 8 DL benchmarks in the multi-objective and single-objective setting. Further, we demonstrate that PriMO remains state-of-the-art at large scales by optimizing a vision-language model over three objectives.

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