Summary of Constrained Best Arm Identification in Grouped Bandits, by Sahil Dharod et al.
Constrained Best Arm Identification in Grouped Bandits
by Sahil Dharod, Malyala Preethi Sravani, Sakshi Heda, Sharayu Moharir
First submitted to arxiv on: 11 Dec 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 proposes a novel approach to solving a grouped bandit setting where each arm consists of multiple independent attributes, each with its own stochastic reward. The goal is to find the most rewarding arm among those with mean rewards exceeding a specified threshold, in a fixed confidence setting. The authors first establish a fundamental limit on policy performance and then introduce a near-optimal confidence interval-based policy, providing analytical guarantees for its effectiveness. Simulations are used to compare the proposed policy’s performance with that of two modified action elimination methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have many different arms to choose from, each with multiple features or attributes that give rewards. Some arms might be more rewarding than others, but only if all their attributes meet a certain threshold. The goal is to find the best arm among those that meet this threshold. To solve this problem, researchers propose a new method that uses confidence intervals and show it’s very effective. They also compare its performance with two other methods. |
