A note on continuous-time online learning
This work provides a theoretical extension for online learning models, but it is incremental as it adapts existing methods to a continuous-time framework.
The paper tackles the problem of extending discrete-time online learning algorithms to continuous-time settings for online linear optimization, adversarial bandit, and adversarial linear bandit, achieving optimal regret bounds with concise proofs.
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.