Summary of Learning For Bandits Under Action Erasures, by Osama Hanna et al.
Learning for Bandits under Action Erasures
by Osama Hanna, Merve Karakas, Lin F. Yang, Christina Fragouli
First submitted to arxiv on: 26 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 A novel multi-arm bandit (MAB) setup is proposed, where the learner must communicate actions to distributed agents over erasure channels, while rewards are directly available through external sensors. The central learner does not receive feedback on whether observed rewards result from desired or erased actions. A scheme is developed to make any existing MAB algorithm robust to action erasures, achieving a worst-case regret that is at most a factor of O(1/) away from the no-erasure worst-case regret. Additionally, a modified successive arm elimination algorithm is proposed, with a worst-case regret of (+K/(1-)), which is shown to be optimal by providing a matching lower bound. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to figure out the best way to make decisions when there’s uncertainty and noise involved. A team of researchers has come up with a new approach to solve this problem, called multi-arm bandit (MAB). In MAB, you need to choose between different options (or “arms”) without knowing which one will give you the best outcome. The twist is that sometimes your choices won’t be recorded correctly, like when you’re trying to communicate with other people or machines over a noisy channel. The researchers developed a special technique to make any existing MAB approach more robust to these errors. This means their method can handle mistakes and still make good decisions. They also came up with a new algorithm that’s really efficient and does well even in situations where there are many options to choose from and the noise is strong. |
