Summary of Active Learning For Fair and Stable Online Allocations, by Riddhiman Bhattacharya et al.
Active Learning for Fair and Stable Online Allocations
by Riddhiman Bhattacharya, Thanh Nguyen, Will Wei Sun, Mohit Tawarmalani
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Other Statistics (stat.OT)
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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 This paper proposes an active learning approach to dynamic fair resource allocation problems, departing from previous work that assumes full feedback from all agents. Instead, the proposed algorithms consider feedback from a select subset of agents at each epoch, providing sub-linear regret bounds for various measures including fairness metrics and stability considerations in matching mechanisms. The key insight lies in adaptively identifying the most informative feedback using dueling upper and lower confidence bounds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a fair way to share resources with others when you don’t have all the information. Usually, people want to make sure everyone gets what they need, but it’s hard because there’s not enough data. The researchers came up with a new way to solve this problem by asking only some of the agents for feedback instead of all of them. This helps them make decisions faster and more efficiently while still making sure everything is fair. |
Keywords
» Artificial intelligence » Active learning
