Summary of Multi-armed Bandits with Abstention, by Junwen Yang et al.
Multi-Armed Bandits with Abstention
by Junwen Yang, Tianyuan Jin, Vincent Y. F. Tan
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); 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 In this paper, researchers develop a new framework that combines two existing concepts: the multi-armed bandit problem and the option to abstain from accepting rewards. The added layer of complexity involves deciding when to accept or abstain from rewards, which affects the overall performance of the algorithm. To tackle this challenge, the authors design and analyze algorithms that achieve optimal performance, both in terms of regret and information-theoretic lower bounds. Their findings provide valuable insights into the benefits of abstention and pave the way for further exploration in online decision-making problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make decisions when you don’t know what will happen next. Imagine you’re playing a game where you can choose one of several options, but sometimes you might not want to get a reward right away. This paper introduces an “abstention” option that lets the decision-maker wait and see if it’s worth taking the risk. The researchers created new algorithms that work well in this situation and showed that they are better than other methods. This is important because it can help us make better decisions in situations where we don’t know what will happen. |
