  Hyperparameter optimization (HPO) is a vital step in improving performance in
deep learning (DL). Practitioners are often faced with the trade-off between
multiple criteria, such as accuracy and latency. Given the high computational
needs of DL and the growing demand for efficient HPO, the acceleration of
multi-objective (MO) optimization becomes ever more important. Despite the
significant body of work on meta-learning for HPO, existing methods are
inapplicable to MO tree-structured Parzen estimator (MO-TPE), a simple yet
powerful MO-HPO algorithm. In this paper, we extend TPE's acquisition function
to the meta-learning setting using a task similarity defined by the overlap of
top domains between tasks. We also theoretically analyze and address the
limitations of our task similarity. In the experiments, we demonstrate that our
method speeds up MO-TPE on tabular HPO benchmarks and attains state-of-the-art
performance. Our method was also validated externally by winning the AutoML
2022 competition on "Multiobjective Hyperparameter Optimization for
Transformers".
