Improved strongly adaptive online learning using coin betting

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

Research output: Contribution to conferencePaperpeer-review

16 Scopus citations

Abstract

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)
StatePublished - 2017
Externally publishedYes
Event20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017 - Fort Lauderdale, United States
Duration: Apr 20 2017Apr 22 2017

Conference

Conference20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017
Country/TerritoryUnited States
CityFort Lauderdale
Period4/20/174/22/17

ASJC Scopus subject areas

  • Artificial Intelligence
  • Statistics and Probability

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