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.
|Title of host publication
|2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022
|Institute of Electrical and Electronics Engineers Inc.
|Published - 2022
|23rd IEEE International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022 - Oulu, Finland
Duration: Jul 4 2022 → Jul 6 2022
|IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
|23rd IEEE International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022
|7/4/22 → 7/6/22
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Computer Science Applications
- Information Systems
- Active Learning
- Bayesian Optimization