A note on continuous-time online learning
Machine Learning
2024-05-20 v1 Machine Learning
Numerical Analysis
Numerical Analysis
Optimization and Control
Abstract
In online learning, the data is provided in a 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.
Cite
@article{arxiv.2405.10399,
title = {A note on continuous-time online learning},
author = {Lexing Ying},
journal= {arXiv preprint arXiv:2405.10399},
year = {2024}
}