@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 = {<p style="text-align: justify;">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.</p>},
issn = {2790-2048},
doi = {https://doi.org/10.4208/jml.240605},
url = {https://global-sci.com/article/91698/a-note-on-continuous-time-online-learning}
}