AI 中文总结
本研究针对大规模学习系统多目标平衡问题，将优化框架扩展至连续状态空间，提出Lazy Graph-LinUCB算法及三种图结构利用机制，可降低异构系统移动成本超三倍且维持相近累积损失。
AI 中文摘要
大规模学习系统常面临平衡公平性、准确率、延迟等多个潜在相互冲突目标的挑战。近期研究将此形式化为二元状态上的优化问题，但许多真实世界控制参数（如公平性阈值、多样性混合率、资源预算）是连续的。本研究将该框架扩展至连续状态空间，将问题建模为最小化线性目标之和，同时受惩罚系统不稳定性的移动成本约束。我们用依赖图（或因子图）捕捉目标的局部结构，每个目标由状态属性子集决定。为解决探索与稳定性的权衡，提出Lazy Graph-LinUCB算法，通过惰性更新最小化切换成本，同时保持近最优悔界。除稳定性外，引入三种利用图结构的高级机制：（1）异步更新调度，消除稀疏图中的同步开销；（2）自适应算法，从数据中学习图结构；（3）联合估计器，利用相关目标间的数据共享显著收紧悔界。实验表明，这些结构利用机制在异构系统中将移动成本降低三倍以上，同时保持相近的累积损失。
英文摘要
Large-scale learning systems often face the challenge of balancing multiple, potentially competing objectives, such as fairness, accuracy, and latency. While recent work has formalized this as an optimization problem over binary states, many real-world control parameters, such as fairness thresholds, diversity mixing rates, or resource budgets, are continuous. In this work, we extend the framework to \emph{continuous state spaces}. We model the problem as minimizing a sum of linear objectives subject to \emph{movement costs} that penalize system instability. We capture the local structure of the objectives using a \emph{dependency graph} (or factor graph), where each objective is determined by a subset of the state attributes. To address the tension between exploration and stability, we propose \emph{Lazy Graph-LinUCB}, an algorithm that performs lazy updates to minimize switching costs while maintaining near-optimal regret. Beyond stability, we introduce three advanced mechanisms to exploit the underlying graph structure: (1) an \emph{asynchronous} update schedule that eliminates synchronization overhead in sparse graphs; (2) an \emph{adaptive} algorithm that learns the graph structure from data; and (3) a \emph{joint estimator} that leverages data sharing among correlated objectives to significantly tighten regret bounds. Empirically, we demonstrate that these structural exploitations reduce movement costs by more than a factor of three in heterogeneous systems while maintaining similar cumulative losses.
