• Corpus ID: 52823321

# Fully Implicit Online Learning

@article{Song2018FullyIO,
title={Fully Implicit Online Learning},
author={Chaobing Song and Ji Liu and Han Liu and Yong Jiang and T. Zhang},
journal={ArXiv},
year={2018},
volume={abs/1809.09350}
}
• Published 25 September 2018
• Computer Science
• ArXiv
Regularized online learning is widely used in machine learning applications. In online learning, performing exact minimization ($i.e.,$ implicit update) is known to be beneficial to the numerical stability and structure of solution. In this paper we study a class of regularized online algorithms without linearizing the loss function or the regularizer, which we call \emph{fully implicit online learning} (FIOL). We show that for arbitrary Bregman divergence, FIOL has the $O(\sqrt{T})$ regret for…

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