A note on continuous-time online learning
<|reference_start|>A note on continuous-time online learning: 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.<|reference_end|>
@article{ying2024a,
    title={A note on continuous-time online learning},
    author={Lexing Ying},
    journal={arXiv preprint arXiv:2405.10399},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.10399},
    primaryClass={stat.ML cs.LG cs.NA math.NA math.OC}
}