新策略搞定图结构非线性关联
该论文针对图结构组合半赌博机问题，开发了基于图因果奖励建模、解析再生核方法和泰勒近似的自适应策略。理论证明在时间上具有次线性性能保证，数据量上线性扩展。实验使用合成和真实交通数据集验证了框架的有效性。
Graph Structured Combinatorial Semi-Bandit with Nonlinear Reward Associations through Separable Signals
The identification of optimal structures within vast arrays of interconnected data necessitates significant sampling- and computational effort. Learning and leveraging underlying signal dependencies can improve efficiency and predictive capabilities considerably, but the ubiquity of nonlinear statistical relations amplifies the complexity of such undertakings. In this paper, we develop novel generic and adaptive strategies equipped with routines for graph-based causal reward modeling, analytic reproducing kernel methods, and Taylor approximation of functional processes. We establish theoretical performance guarantees sublinear in time and linear in data volume over time. Our analyses cover robustness to a multitude of uncertainties arising from noise interference, gradual model convergence, and solution space mismatch. The framework's general appeal is substantiated by a minimalistic set of conditions or reliance on prior estimates, while various outlined modifications address specific or extended settings. To demonstrate practical effectiveness, we conduct numerical experiments using both benchmarked synthetic and real-world transportation datasets.