解决黑箱函数优化中的随机性问题，提升数据效率与鲁棒性。
Offline Stochastic Optimization of Black-Box Objective Functions
- 用可微代理模型或保守场约束梯度估计，应对不确定环境。
- 在真实和合成任务上均优于传统离线优化方法。
- 适合数据稀缺但需高可靠性的科研与工程场景。
科学与工程中的诸多挑战，如药物发现和通信网络设计，涉及在巨大搜索空间中优化复杂且昂贵的黑箱函数。因此，利用已有数据避免高昂的主动查询至关重要。尽管离线黑箱优化（BBO）对确定性问题有效，但在捕捉现实世界中的随机性方面可能不足。为此，我们提出随机离线黑箱优化（SOBBO），同时处理黑箱目标与不可控不确定性。针对大数据场景，采用可微代理模型实现基于梯度的优化；针对小样本场景，通过保守场约束直接估计梯度，提升鲁棒性、收敛性与数据效率。数值实验表明，该方法在合成与真实任务上均具有效性。
原文摘要 · Abstract (English)
Many challenges in science and engineering, such as drug discovery and communication network design, involve optimizing complex and expensive black-box functions across vast search spaces. Thus, it is essential to leverage existing data to avoid costly active queries of these black-box functions. To this end, while Offline Black-Box Optimization (BBO) is effective for deterministic problems, it may fall short in capturing the stochasticity of real-world scenarios. To address this, we introduce Stochastic Offline BBO (SOBBO), which tackles both black-box objectives and uncontrolled uncertainties. We propose two solutions: for large-data regimes, a differentiable surrogate allows for gradient-based optimization, while for scarce-data regimes, we directly estimate gradients under conservative field constraints, improving robustness, convergence, and data efficiency. Numerical experiments demonstrate the effectiveness of our approach on both synthetic and real-world tasks.
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