Summary of Bounds on the Price Of Feedback For Mistake-bounded Online Learning, by Jesse Geneson and Linus Tang
Bounds on the price of feedback for mistake-bounded online learning
by Jesse Geneson, Linus Tang
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Discrete Mathematics (cs.DM); Combinatorics (math.CO)
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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 research paper improves worst-case bounds for various online learning scenarios. Specifically, it enhances upper bounds for delayed ambiguous reinforcement learning and learning compositions of families of functions. The paper also resolves an open problem on the price of bandit feedback in multiclass learning and refines an upper bound on the price of r-input delayed ambiguous reinforcement learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study improves ways to learn new things online. It makes sure that these methods work well even if they’re not perfect. The researchers solved a puzzle about how hard it is to get good results when you only have some information, and they also made an old method better for learning from feedback. |
Keywords
* Artificial intelligence * Online learning * Reinforcement learning
