End-to-End Learning of Deep Kernel Acquisition Functions for Bayesian Optimization

TL;DR

This paper introduces a meta-learning approach for Bayesian optimization that trains neural network-based kernels to directly improve optimization performance across multiple tasks, outperforming existing methods.

Contribution

It proposes a novel meta-learning framework that trains neural network kernels to minimize the gap to the true optimum, enhancing BO performance on unseen tasks.

Findings

Outperforms existing BO methods on text datasets

Effective transfer of knowledge across tasks

Improved optimization accuracy and efficiency

Abstract

For Bayesian optimization (BO) on high-dimensional data with complex structure, neural network-based kernels for Gaussian processes (GPs) have been used to learn flexible surrogate functions by the high representation power of deep learning. However, existing methods train neural networks by maximizing the marginal likelihood, which do not directly improve the BO performance. In this paper, we propose a meta-learning method for BO with neural network-based kernels that minimizes the expected gap between the true optimum value and the best value found by BO. We model a policy, which takes the current evaluated data points as input and outputs the next data point to be evaluated, by a neural network, where neural network-based kernels, GPs, and mutual information-based acquisition functions are used as its layers. With our model, the neural network-based kernel is trained to be appropriate for the acquisition function by backpropagating the gap through the acquisition function and GP. Our model is trained by a reinforcement learning framework from multiple tasks. Since the neural network is shared across different tasks, we can gather knowledge on BO from multiple training tasks, and use the knowledge for unseen test tasks. In experiments using three text document datasets, we demonstrate that the proposed method achieves better BO performance than the existing methods.