  Bayesian optimization (BO) is a popular method to optimize costly black-box
functions. While traditional BO optimizes each new target task from scratch,
meta-learning has emerged as a way to leverage knowledge from related tasks to
optimize new tasks faster. However, existing meta-learning BO methods rely on
surrogate models that suffer from scalability issues and are sensitive to
observations with different scales and noise types across tasks. Moreover, they
often overlook the uncertainty associated with task similarity. This leads to
unreliable task adaptation when only limited observations are obtained or when
the new tasks differ significantly from the related tasks. To address these
limitations, we propose a novel meta-learning BO approach that bypasses the
surrogate model and directly learns the utility of queries across tasks. Our
method explicitly models task uncertainty and includes an auxiliary model to
enable robust adaptation to new tasks. Extensive experiments show that our
method demonstrates strong anytime performance and outperforms state-of-the-art
meta-learning BO methods in various benchmarks.
