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Volume 4, Issue 1
A Note on Continuous-Time Online Learning

Lexing Ying

DOI: 10.4208/jml.240605

J. Mach. Learn. , 4 (2025), pp. 1-10.

Published online: 2025-03

[An open-access article; the PDF is free to any online user.]

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  • Abstract

In online learning, the data is provided in sequential order, and the goal of the learner is to make online decisions to minimize overall regrets. This note is concerned with continuous-time models and algorithms for several online learning problems: online linear optimization, adversarial bandit, and adversarial linear bandit. For each problem, we extend the discrete-time algorithm to the continuous-time setting and provide a concise proof of the optimal regret bound.

  • Keywords

Online learning, Online optimization, Adversarial bandits, Adversarial linear bandits.

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{JML-4-1, author = {Ying , Lexing}, title = {A Note on Continuous-Time Online Learning}, journal = {Journal of Machine Learning}, year = {2025}, volume = {4}, number = {1}, pages = {1--10}, abstract = {

In online learning, the data is provided in sequential order, and the goal of the learner is to make online decisions to minimize overall regrets. This note is concerned with continuous-time models and algorithms for several online learning problems: online linear optimization, adversarial bandit, and adversarial linear bandit. For each problem, we extend the discrete-time algorithm to the continuous-time setting and provide a concise proof of the optimal regret bound.

}, issn = {2790-2048}, doi = {https://doi.org/10.4208/jml.240605}, url = {http://global-sci.org/intro/article_detail/jml/23889.html} }
TY - JOUR T1 - A Note on Continuous-Time Online Learning AU - Ying , Lexing JO - Journal of Machine Learning VL - 1 SP - 1 EP - 10 PY - 2025 DA - 2025/03 SN - 4 DO - http://doi.org/10.4208/jml.240605 UR - https://global-sci.org/intro/article_detail/jml/23889.html KW - Online learning, Online optimization, Adversarial bandits, Adversarial linear bandits. AB -

In online learning, the data is provided in sequential order, and the goal of the learner is to make online decisions to minimize overall regrets. This note is concerned with continuous-time models and algorithms for several online learning problems: online linear optimization, adversarial bandit, and adversarial linear bandit. For each problem, we extend the discrete-time algorithm to the continuous-time setting and provide a concise proof of the optimal regret bound.

Ying , Lexing. (2025). A Note on Continuous-Time Online Learning. Journal of Machine Learning. 4 (1). 1-10. doi:10.4208/jml.240605
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