Summary of A Note on Continuous-time Online Learning, by Lexing Ying
A note on continuous-time online learning
by Lexing Ying
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Numerical Analysis (math.NA); Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents novel continuous-time models and algorithms for several online learning problems, including online linear optimization, adversarial bandit, and adversarial linear bandit. These models aim to minimize overall regrets in sequential decision-making processes. Building upon discrete-time approaches, the authors extend existing algorithms to the continuous-time setting and provide optimal regret bounds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops new models and algorithms for online learning problems that involve making decisions in a sequence. The goal is to make good choices that will minimize any regrets or mistakes made along the way. The authors take existing ideas from discrete time and adapt them to work with continuous time, providing a framework for minimizing regrets. |
Keywords
» Artificial intelligence » Online learning » Optimization
