Summary of Hr-bandit: Human-ai Collaborated Linear Recourse Bandit, by Junyu Cao et al.
HR-Bandit: Human-AI Collaborated Linear Recourse Bandit
by Junyu Cao, Ruijiang Gao, Esmaeil Keyvanshokooh
First submitted to arxiv on: 18 Oct 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 proposed Recourse Linear UCB algorithm () optimizes action selection and feature modifications by balancing exploration and exploitation, mimicking human doctors’ recommendations. It’s an extension of to the Human-AI Linear Recourse Bandit (), which integrates human expertise for enhanced performance. The algorithm offers three key guarantees: warm-start, human-effort minimization, and robustness against suboptimal human decisions. Empirical results, including a healthcare case study, demonstrate its superiority over existing benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes two algorithms that help patients access more effective treatments by modifying their conditions. One algorithm balances exploration and exploitation to recommend actions and features. The other algorithm adds human expertise to make better recommendations. These algorithms ensure good initial performance, minimize the need for human input, and can still perform well even if humans make mistakes. The paper includes a real-life healthcare example that shows how well these algorithms work. |
