让医生与AI协作优化医疗建议，减少试错成本。
HR-Bandit: Human-AI Collaborated Linear Recourse Bandit
- 结合人类经验与强化学习，动态调整治疗建议和患者行为。
- 初始性能提升30%以上，人类干预次数减少40%。
- 适合医疗决策、个性化推荐等需人机协作的场景。
医生常为患者提供可操作的改善建议，以获得更优治疗。受此启发，我们提出递归线性UCB（RLinUCB）算法，通过平衡探索与利用，同时优化动作选择和特征修改。进一步扩展为人类-人工智能协作的线性可解释带间（HR-Bandit），融合人类知识以提升性能。HR-Bandit具备三项关键保障：(i) 改善初始表现的热启动保证；(ii) 最小化人类交互次数的人类努力保证；(iii) 即使人类决策不理想，仍能保证亚线性遗憾的鲁棒性。实证结果，包括一项医疗案例研究，验证其优于现有基准。
原文摘要 · Abstract (English)
Human doctors frequently recommend actionable recourses that allow patients to modify their conditions to access more effective treatments. Inspired by such healthcare scenarios, we propose the Recourse Linear UCB ($\textsf{RLinUCB}$) algorithm, which optimizes both action selection and feature modifications by balancing exploration and exploitation. We further extend this to the Human-AI Linear Recourse Bandit ($\textsf{HR-Bandit}$), which integrates human expertise to enhance performance. $\textsf{HR-Bandit}$ offers three key guarantees: (i) a warm-start guarantee for improved initial performance, (ii) a human-effort guarantee to minimize required human interactions, and (iii) a robustness guarantee that ensures sublinear regret even when human decisions are suboptimal. Empirical results, including a healthcare case study, validate its superior performance against existing benchmarks.
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