A note on continuous-time online learning
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 14 references that are in the map. First posted on arXiv 16 May 2024.
Builds on
3 of 14 references.
References · 3
- 2022Continuous Prediction with Experts' Advicecited by 7
- 2022A PDE-Based Analysis of the Symmetric Two-Armed Bernoulli Banditcited by 3
- 2022Continuous-in-time Limit for Bayesian Banditscited by 2
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