Summary of Chained Information-theoretic Bounds and Tight Regret Rate For Linear Bandit Problems, by Amaury Gouverneur et al.
Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems
by Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering, Mikael Skoglund
First submitted to arxiv on: 5 Mar 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 In this paper, researchers explore the performance of a modified Thompson-Sampling algorithm in solving bandit problems. By building upon previous information-theoretic frameworks, they establish new regret bounds that are dependent on the metric entropy of the action space. This work has implications for applications such as multi-armed bandits and online learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well an improved version of Thompson-Sampling does in solving certain types of problems called bandit problems. It’s based on previous research that used ideas from information theory to understand how algorithms do. The new algorithm works better than before, especially when the possible actions are far apart in a special kind of space. |
Keywords
* Artificial intelligence * Online learning
