Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Problem: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.
Solution:
We perform efficient and flexible transfer learning in the framework of Bayesian optimization through meta-learned neural acquisition functions.