Summary of Stochastic Bandits For Egalitarian Assignment, by Eugene Lim et al.
Stochastic Bandits for Egalitarian Assignment
by Eugene Lim, Vincent Y. F. Tan, Harold Soh
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 new paper tackles the egalitarian assignment problem in stochastic multi-armed bandits, where an agent assigns users to arms to maximize the minimum expected cumulative reward among all users. The EgalMAB framework is designed for fairness in job and resource allocations, with applications beyond. A UCB-based policy, EgalUCB, is proposed and analyzed, along with upper bounds on cumulative regret. Complementing this, an almost-matching policy-independent impossibility result is established. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a new study, researchers explore egalitarian assignment in multi-armed bandits to ensure fairness in job and resource allocations. The goal is to maximize the minimum reward among all users over time. A special algorithm, EgalUCB, is designed to achieve this, along with limits on how much better other approaches could do. This work also shows that some limitations are inherent. |
