Meta-Bayesian optimisation (meta-BO) aims to improve the sample efficiency of
Bayesian optimisation by leveraging data from related tasks. While previous
methods successfully meta-learn either a surrogate model or an acquisition
function independently, joint training of both components remains an open
challenge. This paper proposes the first end-to-end differentiable meta-BO
framework that generalises neural processes to learn acquisition functions via
transformer architectures. We enable this end-to-end framework with
reinforcement learning (RL) to tackle the lack of labelled acquisition data.
Early on, we notice that training transformer-based neural processes from
scratch with RL is challenging due to insufficient supervision, especially when
rewards are sparse. We formalise this claim with a combinatorial analysis
showing that the widely used notion of regret as a reward signal exhibits a
logarithmic sparsity pattern in trajectory lengths. To tackle this problem, we
augment the RL objective with an auxiliary task that guides part of the
architecture to learn a valid probabilistic model as an inductive bias. We
demonstrate that our method achieves state-of-the-art regret results against
various baselines in experiments on standard hyperparameter optimisation tasks
and also outperforms others in the real-world problems of mixed-integer
programming tuning, antibody design, and logic synthesis for electronic design
automation.

该论文提出了一种基于元学习的贝叶斯优化框架，并利用神经过程和转换器结构学习收购函数，通过强化学习来解决元数据缺乏标签问题，最终在多种领域的实验中取得了最先进的效果。