Summary of Efficient Contextual Bandits with Uninformed Feedback Graphs, by Mengxiao Zhang et al.
Efficient Contextual Bandits with Uninformed Feedback Graphs
by Mengxiao Zhang, Yuheng Zhang, Haipeng Luo, Paul Mineiro
First submitted to arxiv on: 12 Feb 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 proposes an efficient and optimal algorithm for contextual bandits with uninformed feedback graphs, which is a critical problem in online learning. The algorithm reduces the uninformed setting to online regression over both losses and graphs, allowing it to learn the graphs using log loss instead of squared loss. This approach yields favorable regret guarantees. The authors demonstrate the effectiveness of their algorithm on a bidding application using synthetic and real-world data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better decisions when we don’t know what will happen next. It solves a problem where we have to choose between many options, but we can only see how well each option did after we’ve chosen it. The researchers developed an algorithm that can handle this kind of situation and tested it on real-life data from bidding applications. |
Keywords
* Artificial intelligence * Online learning * Regression
