Bayesian Active Meta-Learning for Black-Box Optimization

Ivana Nikoloska, Osvaldo Simeone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665494557
DOIs
StatePublished - 2022
Externally publishedYes
Event23rd IEEE International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022 - Oulu, Finland
Duration: Jul 4 2022Jul 6 2022

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume2022-July

Conference

Conference23rd IEEE International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022
Country/TerritoryFinland
CityOulu
Period7/4/227/6/22

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Information Systems

Keywords

  • Active Learning
  • Bayesian Optimization
  • Meta-learning

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