End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes

Understand

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.