Summary of Satisficing Regret Minimization in Bandits, by Qing Feng et al.
Satisficing Regret Minimization in Bandits
by Qing Feng, Tianyi Ma, Ruihao Zhu
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 This paper proposes an algorithm called SELECT for satisficing exploration in bandit optimization, which aims to select arms with mean reward exceeding a certain threshold value as frequently as possible. The algorithm achieves constant satisficing regret for various bandit optimization problems in the realizable case, where a satisficing arm exists. In the non-realizable case, SELECT enjoys the same standard regret guarantee as the oracle. The paper also presents numerical experiments validating the performance of SELECT for popular bandit optimization settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research develops an algorithm to find arms with high rewards in bandit optimization problems. Imagine you’re playing a game where you have to choose between different options, and some options are better than others. The goal is to pick the best option as much as possible. This algorithm, called SELECT, helps you do that by trying out different options and seeing which one gives you the most reward. |
Keywords
» Artificial intelligence » Optimization
