# A note on continuous-time online learning

arXiv: https://arxiv.org/abs/2405.10399 · HTML: https://mlsanity.com/paper/2405.10399/

## 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 (most cited first, 3 of 3)

- [Continuous Prediction with Experts' Advice](https://mlsanity.com/paper/2206.00236/) (2022)
- [A PDE-Based Analysis of the Symmetric Two-Armed Bernoulli Bandit](https://mlsanity.com/paper/2202.05767/) (2022)
- [Continuous-in-time Limit for Bayesian Bandits](https://mlsanity.com/paper/2210.07513/) (2022)

## 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.