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 - <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>