Summary of Optimal Streaming Algorithms For Multi-armed Bandits, by Tianyuan Jin et al.
Optimal Streaming Algorithms for Multi-Armed Bandits
by Tianyuan Jin, Keke Huang, Jing Tang, Xiaokui Xiao
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 explores two versions of the best arm identification (BAI) problem under the streaming model. In this context, we have a stream of n arms with reward distributions supported on [0,1] and unknown means. The algorithm cannot access an arm unless it is stored in a limited-size memory. This means that the algorithm must make decisions based on the information available up to that point in the stream. The paper proposes two variants of the BAI problem and evaluates their performance using various evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to find the best way to identify the most rewarding option when you can’t look back or try everything. Imagine you’re trying to decide which movie to watch, but new movies keep coming out and you can only see a few at a time. The researchers study two ways to make this decision and compare their performance. |
