Summary of Biased Dueling Bandits with Stochastic Delayed Feedback, by Bongsoo Yi et al.
Biased Dueling Bandits with Stochastic Delayed Feedback
by Bongsoo Yi, Yue Kang, Yao Li
First submitted to arxiv on: 26 Aug 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 A novel variation of the multi-armed bandit problem, the dueling bandit problem, has garnered significant attention in recent years due to its widespread applications in online advertising, recommendation systems, information retrieval, and more. The biased dueling bandit problem with stochastic delayed feedback is a partially observable issue that poses a significant challenge to existing literature, as it affects how quickly and accurately the agent can update their policy. This paper introduces two algorithms designed to handle situations involving delay: one requiring complete distribution information and another tailored for unknown distributions with known expected values. The proposed algorithms achieve optimal regret bounds for the dueling bandit problem in the absence of delay. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores a new variation of the multi-armed bandit problem, which is important because it helps us understand how to make better decisions when we can’t get immediate feedback. In real-life situations, like online advertising or recommendation systems, feedback might be delayed or not immediately available. The authors propose two ways to handle this delay: one needs to know the distribution of delays and another only needs to know the average delay. They show that these algorithms work well in both synthetic and real-world scenarios. |
Keywords
» Artificial intelligence » Attention
