Summary of A Unified Framework For Analyzing Meta-algorithms in Online Convex Optimization, by Mohammad Pedramfar et al.
A Unified Framework for Analyzing Meta-algorithms in Online Convex Optimization
by Mohammad Pedramfar, Vaneet Aggarwal
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); Machine Learning (stat.ML)
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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 This paper delves into online convex optimization in diverse scenarios, exploring different feedback types (full-information, semi-bandit, bandit) and regret notions (static adversarial, dynamic, adaptive) in both stochastic and non-stochastic settings. The authors propose a framework to systematically develop and analyze meta-algorithms for these various settings. They demonstrate that any algorithm for online linear optimization with fully adaptive adversaries can be applied to online convex optimization. Additionally, they show that algorithms requiring full-information feedback can be transformed into those using semi-bandit feedback with comparable regret bounds. Furthermore, they reveal that deterministic semi-bandit feedback-based algorithms designed for fully adaptive adversaries can achieve similar results when facing oblivious adversaries using stochastic semi-bandit feedback. The framework enables the analysis of online optimization in multiple settings, recovers existing results with simplified proofs, and yields new findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to optimize online learning in different situations. It looks at various types of feedback (full-information, semi-bandit, bandit) and how regret works in these scenarios. The authors create a framework that helps them develop and test algorithms for these different situations. They show that some algorithms can be used in multiple settings, which makes it easier to learn online. They also find new ways to convert one type of feedback into another without losing performance. |
Keywords
* Artificial intelligence * Online learning * Optimization
