Summary of Incentive-compatible Bandits: Importance Weighting No More, by Julian Zimmert et al.
Incentive-compatible Bandits: Importance Weighting No More
by Julian Zimmert, Teodor V. Marinov
First submitted to arxiv on: 10 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT)
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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 addresses incentive-compatible online learning with bandit feedback, where experts are self-interested agents that might misrepresent their preferences to be selected more often. The goal is to design algorithms that are both incentive-compatible and have no regret relative to the best fixed expert in hindsight. The authors build upon prior work by Freeman et al. (2020), proposing an algorithm with optimal O() regret in the full information setting and O(T{2/3}(K(K)){1/3}) regret in the bandit setting, where T is the number of rounds and K is the number of experts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a way for online learning systems to work fairly with self-interested experts who might try to influence the outcome. The goal is to create algorithms that are both fair and don’t regret choosing a different expert in hindsight. The authors improve upon previous work by developing an algorithm that achieves this balance, ensuring that experts have no regrets and the system works fairly. |
Keywords
» Artificial intelligence » Online learning
