GP-UCB 用于约束型黑盒杠杆优化的实战研究
Gaussian Process Bandits for Constraint Black Box Lever Optimization
一篇把 GP-UCB 搬进生产环境的论文，实测比 ε-greedy 显著更省探索成本，做预算分配优化的工程师可以直接参考核函数选型。
论文针对共享预算下离散分配权重优化问题，采用 Gaussian Process Upper Confidence Bound（GP-UCB）算法，在奖励函数上放置高斯过程先验，让相邻配置之间共享信息，减少探索量。作者先用高保真合成模拟器在平稳、漂移和反应式三类环境下测试核函数配置，发现 Matérn + Linear 组合核最稳健。随后在生产环境开展了大规模随机对照试验（RCT），结果显示 GP-UCB 相比标准 ε-greedy 基线带来统计显著的效率提升。
Gaussian Process Bandits for Constraint Black Box Lever Optimization
Optimizing discrete allocation weights across competing segments of a shared budget is a fundamental challenge in resource-constrained, sequential decision-making environments. In practice, this optimization is often hindered by delayed, noisy, and aggregate feedback, alongside strict operational constraints on how frequently weights can be adjusted. Because of the resulting combinatorial action space and extreme data sparsity, classical multi-armed bandit algorithms that evaluate arms independently are rendered highly inefficient. To overcome these limitations, we propose a segmentation-agnostic approach utilizing Gaussian Process Upper Confidence Bound (GP-UCB) algorithms. By placing a Gaussian Process prior on the reward function, our method effectively shares information across neighboring configurations, drastically reducing the necessary exploration horizon. In this paper, we bridge the gap between theoretical bandit research and production-scale deployment. We first introduce a high-fidelity synthetic simulator to evaluate various kernel configurations under stationary, drifting, and reactive environments, identifying a composite Matérn + Linear kernel as the most robust choice for overcoming local noise and global shifts. Finally, we validate our findings through a large-scale Randomized Controlled Trial (RCT) in a live production environment. Our empirical results demonstrate that the GP-UCB approach delivers a statistically significant efficiency lift compared to a standard $\varepsilon$-greedy baseline, offering a highly practical solution for real-world black-box lever optimization.