Summary of Rate-preserving Reductions For Blackwell Approachability, by Christoph Dann et al.
Rate-Preserving Reductions for Blackwell Approachability
by Christoph Dann, Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 explores the connection between Blackwell approachability and no-regret learning, showing that algorithms solving one problem can be converted to solve the other. Building on Abernethy et al.’s (2011) work, this study delves into the conditions under which a reduction between these problems preserves not only sublinear convergence but also optimal rates of convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, researchers are trying to understand how two related concepts in machine learning – Blackwell approachability and no-regret learning – are connected. They found that if you can solve one problem, you can also solve the other, but now they’re looking at what it takes to make sure the solution is not only good but also the best possible. |
Keywords
» Artificial intelligence » Machine learning
