Bayesian Active Meta-Learning for Black-Box Optimization

TL;DR

This paper introduces an information-theoretic active task selection method for Bayesian meta-learning, significantly reducing labeling efforts in data-efficient black-box optimization scenarios such as wireless system deployment.

Contribution

It proposes a novel active task selection mechanism based on information theory to improve meta-learning efficiency in black-box optimization tasks.

Findings

Reduces the number of labeled tasks needed for effective meta-learning.

Enhances Bayesian optimization performance with fewer labeled data.

Demonstrates effectiveness in wireless system deployment scenarios.

Abstract

Data-efficient learning algorithms are essential in many practical applications for which data collection is expensive, e.g., for the optimal deployment of wireless systems in unknown propagation scenarios. Meta-learning can address this problem by leveraging data from a set of related learning tasks, e.g., from similar deployment settings. In practice, one may have available only unlabeled data sets from the related tasks, requiring a costly labeling procedure to be carried out before use in meta-learning. For instance, one may know the possible positions of base stations in a given area, but not the performance indicators achievable with each deployment. To decrease the number of labeling steps required for meta-learning, this paper introduces an information-theoretic active task selection mechanism, and evaluates an instantiation of the approach for Bayesian optimization of black-box models.