Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
This work addresses the challenge of transferring knowledge across tasks for more efficient optimization, which is incremental as it builds on Bayesian optimization and Gaussian processes.
The paper tackles the problem of data-efficiency in global black-box optimization by proposing a transfer learning method that meta-trains acquisition functions using reinforcement learning on related tasks, resulting in improved performance on simulation-to-real and synthetic tasks, with the algorithm identifying structural properties and performing well in various data settings.
Transferring knowledge across tasks to improve data-efficiency is one of the open key challenges in the field of global black-box optimization. Readily available algorithms are typically designed to be universal optimizers and, therefore, 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 simulation-to-real transfer task as well as on several synthetic functions and on 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 particular structure is present.