Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
Transferring knowledge across tasks to improve data-efficiency is one ofthe open key challenges in the area of global optimization algorithms.Readilyavailable algorithms are typically designed to be universal optimizers and, thus,often suboptimal for specific tasks.We propose a novel transfer learning method toobtain customized optimizers within the well-established framework of Bayesianoptimization, allowing our algorithm to utilize the proven generalizationcapabilities of Gaussian processes.Using reinforcement learning to meta-train anacquisition function on a set of related tasks, the proposed method learns toextract 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 simulatedfunctions and two hyperparameter search problems.The results show that ouralgorithm automatically identifies structural properties of objective functionsfrom available source tasks or simulations, performs favourably in settings withboth scarse and abundant source data, and falls back to the performance levelof 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.