Bayesian Active Meta-Learning for Black-Box Optimization

TY - CHAP

T1 - Bayesian Active Meta-Learning for Black-Box Optimization

AU - Nikoloska, Ivana

AU - Simeone, Osvaldo

N1 - Funding Information: This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Program (Grant Agreement No. 725731). Funding Information: This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Program (Grant Agreement No. 725731) Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Active Learning

KW - Bayesian Optimization

KW - Meta-learning

UR - http://www.scopus.com/inward/record.url?scp=85136008948&partnerID=8YFLogxK

U2 - 10.1109/SPAWC51304.2022.9833993

DO - 10.1109/SPAWC51304.2022.9833993

M3 - Conference paper

AN - SCOPUS:85136008948

T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC

BT - 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 23rd IEEE International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022

Y2 - 4 July 2022 through 6 July 2022

ER -