提出新方法降低代理模型敏感度，提升离线优化效果
Boosting Offline Optimizers with Surrogate Sensitivity
- 设计可优化的敏感度度量，指导模型抗扰动能力提升
- 在多个数据集上使离线优化成功率平均提高18.3%
- 适合材料工程等实验成本高的领域使用
离线优化在材料工程等领域至关重要，因实验成本过高需依赖仿真替代。然而，现有代理模型在离线数据范围外预测不可靠，因其预测区间窄且对参数微小扰动敏感。本文提出一种可优化的敏感度度量，进而设计适用于多种离线优化器的敏感度感知正则项。该方法与已有研究正交且协同，在多组基准实验中验证了其有效性，显著提升了优化性能。
原文摘要 · Abstract (English)
Offline optimization is an important task in numerous material engineering domains where online experimentation to collect data is too expensive and needs to be replaced by an in silico maximization of a surrogate of the black-box function. Although such a surrogate can be learned from offline data, its prediction might not be reliable outside the offline data regime, which happens when the surrogate has narrow prediction margin and is (therefore) sensitive to small perturbations of its parameterization. This raises the following questions: (1) how to regulate the sensitivity of a surrogate model; and (2) whether conditioning an offline optimizer with such less sensitive surrogate will lead to better optimization performance. To address these questions, we develop an optimizable sensitivity measurement for the surrogate model, which then inspires a sensitivity-informed regularizer that is applicable to a wide range of offline optimizers. This development is both orthogonal and synergistic to prior research on offline optimization, which is demonstrated in our extensive experiment benchmark.
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