Summary of An Adaptive Approach For Infinitely Many-armed Bandits Under Generalized Rotting Constraints, by Jung-hun Kim et al.
An Adaptive Approach for Infinitely Many-armed Bandits under Generalized Rotting Constraints
by Jung-hun Kim, Milan Vojnovic, Se-Young Yun
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper explores infinitely many-armed bandit problems in a unique setting where arm rewards decay over time. The authors introduce an algorithm that adapts to this reward degradation using UCB with a sliding window approach. This algorithm achieves tight regret bounds for both slow and abrupt rotting scenarios, as demonstrated through numerical experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at a special kind of problem called the infinitely many-armed bandit. Imagine you’re trying to find the best way to get rewards from different options, but over time those options start to get worse. The authors came up with a new algorithm that helps us deal with this problem. They tested it and showed it can be very good at finding the best option even when things are getting worse. |
