A note on continuous-time online learning

Lexing Ying
5/16/2024
stat.MLcs.LGmath.NA

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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.

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Cite this paper

@article{ying2024note,
  title  = {A note on continuous-time online learning},
  author = {Lexing Ying},
  year   = {2024},
  eprint = {2405.10399},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.10399v1}
}

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