A note on continuous-time online learning
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913353671114752 |
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| author | Ying, Lexing |
| author_facet | Ying, Lexing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10399 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A note on continuous-time online learning Ying, Lexing Machine Learning Numerical Analysis Optimization and Control 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. |
| title | A note on continuous-time online learning |
| topic | Machine Learning Numerical Analysis Optimization and Control |
| url | https://arxiv.org/abs/2405.10399 |