Online Learning Adaptive to Dynamic and Adversarial Environments

Yanning Shen, Tianyi Chen, Georgios B Giannakis

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

Abstract

The present contribution deals with online learning of functions, where multi-kernel approaches, among other popular methods, have well-documented merits, but also face major challenges such as scalability and adaptivity. Leveraging the random feature approximation, an online multi-kernel learning scheme is developed to infer the intended nonlinear function. To account for dynamic and possibly adversarial environments, an adaptive and scalable multi-kernel learning scheme is also introduced at affordable complexity and memory requirements. Performance guarantees are provided in terms of dynamic regret analysis, while numerical tests on a Twitter dataset are carried out to showcase the effectiveness of our approach.

Original languageEnglish (US)
Title of host publication2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538635124
DOIs
StatePublished - Aug 24 2018
Event19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018 - Kalamata, Greece
Duration: Jun 25 2018Jun 28 2018

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume2018-June

Other

Other19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018
CountryGreece
CityKalamata
Period6/25/186/28/18

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