Improved strongly adaptive online learning using coin betting

Kwang Sung Jun, Francesco Orabona, Stephen Wright, Rebecca Willett

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

41 Scopus citations


This paper describes a new parameter-free online learning algorithm for changing environments. In comparing against algorithms with the same time complexity as ours, we obtain a strongly adaptive regret bound that is a factor of at least √log(T) better, where T is the time horizon. Empirical results show that our algorithm outperforms state-of-the-art methods in learning with expert advice and metric learning scenarios.
Original languageEnglish (US)
Title of host publicationProceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017
StatePublished - Jan 1 2017
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-25


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