Transferring knowledge across tasks to improve data-efficiency is one of
 the open key challenges in the area of global optimization algorithms. Readily
 available algorithms are typically designed to be universal optimizers and, thus,
 often suboptimal for specific tasks. We propose a novel transfer learning method to
 obtain customized optimizers within the well-established framework of Bayesian
 optimization, allowing our algorithm to utilize the proven generalization
 capabilities of Gaussian processes. Using reinforcement learning to meta-train an
 acquisition function (AF) on a set of related tasks, the proposed method learns to
 extract implicit structural information and to exploit it for improved data-efficiency.
 We present experiments on a sim-to-real transfer task as well as on several simulated
 functions and two hyperparameter search problems. The results show that our
 algorithm (1) automatically identifies structural properties of objective functions
 from available source tasks or simulations, (2) performs favourably in settings with
 both scarse and abundant source data, and (3) falls back to the performance level
 of general AFs if no structure is present.
We perform efficient and flexible transfer learning in the framework of Bayesian optimization through meta-learned neural acquisition functions. The authors present MetaBO which uses reinforcement learning to meta-learn the acquisition function for Bayesian Optimization, showing increasing sample efficiency on new tasks. The authors propose a meta-learning based alternative to standard acquisition functions (AFs), whereby a pretrained neural network outputs acquisition values as a function of hand-chosen features.