Summary of Adversarial Multi-dueling Bandits, by Pratik Gajane
Adversarial Multi-dueling Bandits
by Pratik Gajane
First submitted to arxiv on: 18 Jun 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 The paper introduces the problem of regret minimization in adversarial multi-dueling bandits, where the learner selects multiple arms and observes feedback on the most preferred arm. The authors propose a novel algorithm, MiDEX (Multi Dueling EXP3), to learn from this preference feedback. They prove that the expected cumulative regret of MiDEX is upper bounded by O((K log K)^1/3 T^2/3) and demonstrate that it is near-optimal with a lower bound of Ω(K^1/3 T^2/3). The authors’ approach has implications for applications in recommendation systems, advertising, and other domains where users interact with multiple options. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies how to make good choices when faced with many options. Imagine you’re at a restaurant with many delicious dishes, and you want to choose the best ones. But what if someone else is giving you feedback on which dish they like most? The authors create an algorithm called MiDEX that helps you learn from this feedback and make good choices. They show that their algorithm is very good at making decisions, and it’s almost as good as if you knew the preferences of all the people helping you. |
